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Collaborative Research: Advancing Semiconductor Education through Expansion and Diversification (ASEED)

NSF

08/15/2024

06/30/2026

{'Value': 'Standard Grant'}

{'Code': '11060000', 'Directorate': {'Abbreviation': 'EDU', 'LongName': 'Directorate for STEM Education'}, 'Division': {'Abbreviation': 'EES', 'LongName': 'Div. of Equity for Excellence in STEM'}}

{'SignBlockName': 'Tori Rhoulac Smith', 'PO_EMAI': 'tosmith@nsf.gov', 'PO_PHON': '7032922315'}

Collaborative Research: Advancing Semiconductor Education through Expansion and <br/>Diversification (ASEED) <br/><br/>Abstract <br/><br/>The Advancing Semiconductor Education through Expansion and Diversification (ASEED) initiative is a collaborative between three Historically Black Colleges and Universities (HBCUs): Prairie View A&M University (PVAMU), Central State University (CSU), and Alabama A&M University (AAMU). This collaborative aims to identify solutions and increase nationwide awareness of the challenges most minority-serving institutions face in semiconductor research and education, including access to proper training, maintaining state-of-the-art costly facilities, and assessing community impacts. ASEED’s goals focus on comprehensive research and education covering all aspects of chip manufacturing: materials science, integrated circuit design, and fabrication & characterization. Supporting activities such as curriculum enhancement, outreach, and knowledge transfer, ASEED integrates faculty training, graduate pathways, and a culturally responsive framework to foster diversity and inclusion. Faculty training programs will equip educators with industry-relevant knowledge, graduate pathways will facilitate student entry into the workforce, and a culturally responsive framework will enhance support for diversity and inclusion. <br/><br/>The Advancing Semiconductor Education through Expansion and Diversification (ASEED) initiative aims to bridge the gap in the semiconductor workforce by training graduates in cutting-edge technology and relevant industry skills. This collaborative effort includes three minority-serving institutions and industry partners, fostering a diverse and innovative semiconductor sector. ASEED focuses on three key areas: 1) 2D semiconductor nanomaterials; 2) AI chip design; and 3) GaN-Based Ultra-Wide Band Gap semiconductor devices. Each partner institution leads a specific research area while contributing to collective goals. This decentralized execution and centralized synthesis model is scalable and allows additional partners to join ASEED. Results from these research areas will enhance academic offerings, including new certificate programs and specialized tracks at both undergraduate and graduate levels, enriched with hands-on lab experiences and practical projects. Additionally, ASEED’s partnerships with leading semiconductor firms provide students with valuable industry exposure and career opportunities. By collaborating with industry experts, ASEED aims to create seamless educational pathways in STEM, from community colleges to advanced degrees, cultivating a skilled and diverse workforce in semiconductor technology.<br/><br/><br/>This project is funded by the NSF Eddie Bernice Johnson INCLUDES Initiative, which motivates and accelerates collaboration for systems change to broaden participation in STEM to the full spectrum of diverse talent, to advance the goals of the CHIPS and Science Act of 2022. This project is also funded by the Historically Black Colleges and Universities Undergraduate Program (HBCU-UP), which provides awards to strengthen STEM undergraduate education and research at HBCUs, and the Louis Stokes Alliances for Minority Participation (LSAMP), which aims to increase STEM degrees to underrepresented populations and supporting research on STEM participation and assessment of LSAMP program impacts.<br/><br/>This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

08/05/2024

08/05/2024

None

Grant

47.076

1

4900

4900

2436203

{'FirstName': 'Suxia', 'LastName': 'Cui', 'PI_MID_INIT': None, 'PI_SUFX_NAME': None, 'PI_FULL_NAME': 'Suxia Cui', 'EmailAddress': 'sucui@pvamu.edu', 'NSF_ID': '000354081', 'StartDate': '08/05/2024', 'EndDate': None, 'RoleCode': 'Principal Investigator'}

{'Name': 'Prairie View A & M University', 'CityName': 'PRAIRIE VIEW', 'ZipCode': '774460519', 'PhoneNumber': '9362611689', 'StreetAddress': '100 UNIVERSITY DR', 'StreetAddress2': None, 'CountryName': 'United States', 'StateName': 'Texas', 'StateCode': 'TX', 'CONGRESSDISTRICT': '10', 'CONGRESS_DISTRICT_ORG': 'TX10', 'ORG_UEI_NUM': 'FTAAW94S6LC6', 'ORG_LGL_BUS_NAME': 'PRAIRIE VIEW A&M UNIVERSITY', 'ORG_PRNT_UEI_NUM': 'FTAAW94S6LC6'}

{'Name': 'Prairie View A & M University', 'CityName': 'PRAIRIE VIEW', 'StateCode': 'TX', 'ZipCode': '77446', 'StreetAddress': '100 UNIVERSITY DR', 'CountryCode': 'US', 'CountryName': 'United States', 'StateName': 'Texas', 'CountryFlag': '1', 'CONGRESSDISTRICT': '10', 'CONGRESS_DISTRICT_PERF': 'TX10'}

{'Code': '032Y00', 'Text': 'Eddie Bernice Johnson INCLUDES'}

2024~400000

{'url': 'https://www.nsf.gov/awardsearch/download?DownloadFileName=2024&All=true', 'xml': '2436203.xml'}

Collaborative Research: Advancing Semiconductor Education through Expansion and Diversification (ASEED)

NSF

08/15/2024

07/31/2026

{'Value': 'Standard Grant'}

{'Code': '11060000', 'Directorate': {'Abbreviation': 'EDU', 'LongName': 'Directorate for STEM Education'}, 'Division': {'Abbreviation': 'EES', 'LongName': 'Div. of Equity for Excellence in STEM'}}

{'SignBlockName': 'Tori Rhoulac Smith', 'PO_EMAI': 'tosmith@nsf.gov', 'PO_PHON': '7032922315'}

The Advancing Semiconductor Education through Expansion and Diversification (ASEED) initiative is a collaborative between three Historically Black Colleges and Universities (HBCUs): Prairie View A&M University (PVAMU), Central State University (CSU), and Alabama A&M University (AAMU). This collaborative aims to identify solutions and increase nationwide awareness of the challenges most minority-serving institutions face in semiconductor research and education, including access to proper training, maintaining state-of-the-art costly facilities, and assessing community impacts. ASEED’s goals focus on comprehensive research and education covering all aspects of chip manufacturing: materials science, integrated circuit design, and fabrication & characterization. Supporting activities such as curriculum enhancement, outreach, and knowledge transfer, ASEED integrates faculty training, graduate pathways, and a culturally responsive framework to foster diversity and inclusion. Faculty training programs will equip educators with industry-relevant knowledge, graduate pathways will facilitate student entry into the workforce, and a culturally responsive framework will enhance support for diversity and inclusion. <br/><br/>The Advancing Semiconductor Education through Expansion and Diversification (ASEED) initiative aims to bridge the gap in the semiconductor workforce by training graduates in cutting-edge technology and relevant industry skills. This collaborative effort includes three minority-serving institutions and industry partners, fostering a diverse and innovative semiconductor sector. ASEED focuses on three key areas: 1) 2D semiconductor nanomaterials; 2) AI chip design; and 3) GaN-Based Ultra-Wide Band Gap semiconductor devices. Each partner institution leads a specific research area while contributing to collective goals. This decentralized execution and centralized synthesis model is scalable and allows additional partners to join ASEED. Results from these research areas will enhance academic offerings, including new certificate programs and specialized tracks at both undergraduate and graduate levels, enriched with hands-on lab experiences and practical projects. Additionally, ASEED’s partnerships with leading semiconductor firms provide students with valuable industry exposure and career opportunities. By collaborating with industry experts, ASEED aims to create seamless educational pathways in STEM, from community colleges to advanced degrees, cultivating a skilled and diverse workforce in semiconductor technology.<br/><br/><br/>This project is funded by the NSF Eddie Bernice Johnson INCLUDES Initiative, which motivates and accelerates collaboration for systems change to broaden participation in STEM to the full spectrum of diverse talent, to advance the goals of the CHIPS and Science Act of 2022. This project is also funded by the Historically Black Colleges and Universities Undergraduate Program (HBCU-UP), which provides awards to strengthen STEM undergraduate education and research at HBCUs, and the Louis Stokes Alliances for Minority Participation (LSAMP), which aims to increase STEM degrees to underrepresented populations and supporting research on STEM participation and assessment of LSAMP program impacts.<br/><br/>This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

08/05/2024

08/05/2024

None

Grant

47.076

1

4900

4900

2436204

[{'FirstName': 'Mohammadreza', 'LastName': 'Hadizadeh', 'PI_MID_INIT': None, 'PI_SUFX_NAME': None, 'PI_FULL_NAME': 'Mohammadreza Hadizadeh', 'EmailAddress': 'mhadizadeh@centralstate.edu', 'NSF_ID': '000755222', 'StartDate': '08/05/2024', 'EndDate': None, 'RoleCode': 'Principal Investigator'}, {'FirstName': 'Mubbashar', 'LastName': 'Khan', 'PI_MID_INIT': 'A', 'PI_SUFX_NAME': None, 'PI_FULL_NAME': 'Mubbashar A Khan', 'EmailAddress': 'mkhan1@centralstate.edu', 'NSF_ID': '000932239', 'StartDate': '08/05/2024', 'EndDate': None, 'RoleCode': 'Co-Principal Investigator'}]

{'Name': 'Central State University', 'CityName': 'WILBERFORCE', 'ZipCode': '453845800', 'PhoneNumber': '5133766011', 'StreetAddress': '1400 BRUSH ROW RD', 'StreetAddress2': None, 'CountryName': 'United States', 'StateName': 'Ohio', 'StateCode': 'OH', 'CONGRESSDISTRICT': '10', 'CONGRESS_DISTRICT_ORG': 'OH10', 'ORG_UEI_NUM': 'UZUVJXMDNZY6', 'ORG_LGL_BUS_NAME': 'CENTRAL STATE UNIVERSITY', 'ORG_PRNT_UEI_NUM': None}

{'Name': 'Central State University', 'CityName': 'WILBERFORCE', 'StateCode': 'OH', 'ZipCode': '453845800', 'StreetAddress': '1400 BRUSH ROW RD', 'CountryCode': 'US', 'CountryName': 'United States', 'StateName': 'Ohio', 'CountryFlag': '1', 'CONGRESSDISTRICT': '10', 'CONGRESS_DISTRICT_PERF': 'OH10'}

{'Code': '913300', 'Text': 'Alliances-Minority Participat.'}

2024~299977

{'url': 'https://www.nsf.gov/awardsearch/download?DownloadFileName=2024&All=true', 'xml': '2436204.xml'}

Collaborative Research: Advancing Semiconductor Education through Expansion and Diversification (ASEED)

NSF

08/15/2024

07/31/2026

{'Value': 'Standard Grant'}

{'Code': '11060000', 'Directorate': {'Abbreviation': 'EDU', 'LongName': 'Directorate for STEM Education'}, 'Division': {'Abbreviation': 'EES', 'LongName': 'Div. of Equity for Excellence in STEM'}}

{'SignBlockName': 'Tori Rhoulac Smith', 'PO_EMAI': 'tosmith@nsf.gov', 'PO_PHON': '7032922315'}

The Advancing Semiconductor Education through Expansion and Diversification (ASEED) initiative is a collaborative between three Historically Black Colleges and Universities (HBCUs): Prairie View A&M University (PVAMU), Central State University (CSU), and Alabama A&M University (AAMU). This collaborative aims to identify solutions and increase nationwide awareness of the challenges most minority-serving institutions face in semiconductor research and education, including access to proper training, maintaining state-of-the-art costly facilities, and assessing community impacts. ASEED’s goals focus on comprehensive research and education covering all aspects of chip manufacturing: materials science, integrated circuit design, and fabrication & characterization. Supporting activities such as curriculum enhancement, outreach, and knowledge transfer, ASEED integrates faculty training, graduate pathways, and a culturally responsive framework to foster diversity and inclusion. Faculty training programs will equip educators with industry-relevant knowledge, graduate pathways will facilitate student entry into the workforce, and a culturally responsive framework will enhance support for diversity and inclusion. <br/><br/>The Advancing Semiconductor Education through Expansion and Diversification (ASEED) initiative aims to bridge the gap in the semiconductor workforce by training graduates in cutting-edge technology and relevant industry skills. This collaborative effort includes three minority-serving institutions and industry partners, fostering a diverse and innovative semiconductor sector. ASEED focuses on three key areas: 1) 2D semiconductor nanomaterials; 2) AI chip design; and 3) GaN-Based Ultra-Wide Band Gap semiconductor devices. Each partner institution leads a specific research area while contributing to collective goals. This decentralized execution and centralized synthesis model is scalable and allows additional partners to join ASEED. Results from these research areas will enhance academic offerings, including new certificate programs and specialized tracks at both undergraduate and graduate levels, enriched with hands-on lab experiences and practical projects. Additionally, ASEED’s partnerships with leading semiconductor firms provide students with valuable industry exposure and career opportunities. By collaborating with industry experts, ASEED aims to create seamless educational pathways in STEM, from community colleges to advanced degrees, cultivating a skilled and diverse workforce in semiconductor technology.<br/><br/><br/>This project is funded by the NSF Eddie Bernice Johnson INCLUDES Initiative, which motivates and accelerates collaboration for systems change to broaden participation in STEM to the full spectrum of diverse talent, to advance the goals of the CHIPS and Science Act of 2022. This project is also funded by the Historically Black Colleges and Universities Undergraduate Program (HBCU-UP), which provides awards to strengthen STEM undergraduate education and research at HBCUs, and the Louis Stokes Alliances for Minority Participation (LSAMP), which aims to increase STEM degrees to underrepresented populations and supporting research on STEM participation and assessment of LSAMP program impacts.<br/><br/>This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

08/05/2024

08/05/2024

None

Grant

47.076

1

4900

4900

2436205

[{'FirstName': 'Zhigang', 'LastName': 'Xiao', 'PI_MID_INIT': None, 'PI_SUFX_NAME': None, 'PI_FULL_NAME': 'Zhigang Xiao', 'EmailAddress': 'zhigang.xiao@aamu.edu', 'NSF_ID': '000484336', 'StartDate': '08/05/2024', 'EndDate': None, 'RoleCode': 'Principal Investigator'}, {'FirstName': 'Xiang', 'LastName': 'Zhao', 'PI_MID_INIT': None, 'PI_SUFX_NAME': None, 'PI_FULL_NAME': 'Xiang Zhao', 'EmailAddress': 'xiang.zhao@aamu.edu', 'NSF_ID': '000353507', 'StartDate': '08/05/2024', 'EndDate': None, 'RoleCode': 'Co-Principal Investigator'}, {'FirstName': 'Satilmis', 'LastName': 'Budak', 'PI_MID_INIT': None, 'PI_SUFX_NAME': None, 'PI_FULL_NAME': 'Satilmis Budak', 'EmailAddress': 'satilmis.budak@aamu.edu', 'NSF_ID': '000523826', 'StartDate': '08/05/2024', 'EndDate': None, 'RoleCode': 'Co-Principal Investigator'}, {'FirstName': 'Shujun', 'LastName': 'Yang', 'PI_MID_INIT': None, 'PI_SUFX_NAME': None, 'PI_FULL_NAME': 'Shujun Yang', 'EmailAddress': 'shujun.yang@aamu.edu', 'NSF_ID': '000614722', 'StartDate': '08/05/2024', 'EndDate': None, 'RoleCode': 'Co-Principal Investigator'}, {'FirstName': 'Qunying', 'LastName': 'Yuan', 'PI_MID_INIT': None, 'PI_SUFX_NAME': None, 'PI_FULL_NAME': 'Qunying Yuan', 'EmailAddress': 'qunying.yuan@aamu.edu', 'NSF_ID': '000735742', 'StartDate': '08/05/2024', 'EndDate': None, 'RoleCode': 'Co-Principal Investigator'}]

{'Name': 'Alabama A&M University', 'CityName': 'NORMAL', 'ZipCode': '357627500', 'PhoneNumber': '2563728186', 'StreetAddress': '4900 MERIDIAN STREET NW', 'StreetAddress2': None, 'CountryName': 'United States', 'StateName': 'Alabama', 'StateCode': 'AL', 'CONGRESSDISTRICT': '05', 'CONGRESS_DISTRICT_ORG': 'AL05', 'ORG_UEI_NUM': 'JDVGS67MSLH7', 'ORG_LGL_BUS_NAME': 'ALABAMA A & M UNIVERSITY', 'ORG_PRNT_UEI_NUM': None}

{'Name': 'Alabama A&M University', 'CityName': 'NORMAL', 'StateCode': 'AL', 'ZipCode': '357627500', 'StreetAddress': '4900 MERIDIAN STREET NW', 'CountryCode': 'US', 'CountryName': 'United States', 'StateName': 'Alabama', 'CountryFlag': '1', 'CONGRESSDISTRICT': '05', 'CONGRESS_DISTRICT_PERF': 'AL05'}

{'Code': '159400', 'Text': 'Hist Black Colleges and Univ'}

2024~300000

{'url': 'https://www.nsf.gov/awardsearch/download?DownloadFileName=2024&All=true', 'xml': '2436205.xml'}

Collaborative Research: FDT-BioTech: Advancing Mathematical and Statistical Foundations to Enhance Human Digital Twin of Neurophysiological Modeling and Uncertainty Quantification

NSF

01/01/2025

12/31/2027

{'Value': 'Standard Grant'}

{'Code': '03040000', 'Directorate': {'Abbreviation': 'MPS', 'LongName': 'Direct For Mathematical & Physical Scien'}, 'Division': {'Abbreviation': 'DMS', 'LongName': 'Division Of Mathematical Sciences'}}

{'SignBlockName': 'Zhilan Feng', 'PO_EMAI': 'zfeng@nsf.gov', 'PO_PHON': '7032927523'}

This project aims to develop the mathematical foundations for a digital twin (DT) system for individuals with autism spectrum disorder (ASD), focusing on dynamic modeling, prediction, uncertainty quantification, and treatment or intervention recommendation through DT-based optimization. ASD is characterized by challenges in social interaction, communication, and behavior, such as difficulties in forming relationships, understanding nonverbal cues, speech development, repetitive behaviors, and sensory sensitivities. The project will create a unified system integrating clinical and neuro-developmental data, analyzed using a DT healthcare paradigm. The DT technology will enable individualized models, and its predictive capabilities will allow healthcare providers to anticipate progression and adjust treatment or intervention proactively. Additionally, the continuous feedback loop from real-time data will enhance therapeutic outcomes. The developed methods and theories will have broader applicability to other medical areas, improving healthcare efficiency, reducing system burdens, and informing public health strategies. This will ultimately enhance care and promote community well-being. The project will also develop quality cyberinfrastructure to share algorithms, data, and open-source software with the community. Furthermore, the investigators plan to expand scientific impacts through collaborating with medical experts and industry scientists, training undergraduate and graduate students, and integrating research findings into course development.<br/><br/>The project will develop a DT framework by modeling brain activities with a unified data structure, linked to behavioral characteristics and interventions aligned with individuals' neuro-developmental processes. This system will integrate multimodal and multi-source data related to human health and development. It will establish foundational models for training and generating synthetic data from DT models, enabling personalized predictions of progression and uncertainty quantification through novel interdisciplinary approaches. The DT system consists of four research modules: (1) Develop computational models based on conditional variational auto-encoders (CVAE) and longitudinal CVAE to analyze brain activities, integrate diverse imaging data, and model neurodevelopmental processes. (2) Create a novel bilevel formulation for multi-distribution fine-tuning techniques on pretrained foundational models and a fast algorithm to learn from heterogeneous data sources to predict ASD outcomes. (3) Develop a model-free conformal prediction procedure to ensemble predictions from multiple models obtained with different modalities and progression simulations, integrating various types of uncertainties into one framework. (4) Develop a DT-based reinforcement learning framework to recommend personalized treatment/intervention plans that significantly improve online learning efficiency and clinical outcomes. The project will address challenges such as multimodality and multi-source data, high-dimensional features, dynamic progression of ASD symptoms, brain functional connectivity, and the need for personalized intervention or treatment recommendations and uncertainty quantification.<br/><br/>This project is jointly funded by the Division of Mathematical Sciences, the OAC Cyberinfrastructure for Sustained Scientific Innovation (CSSI) program, and the CBET Engineering of Biomedical Systems program.<br/><br/>This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

08/20/2024

08/20/2024

None

Grant

47.041, 47.049, 47.070

1

4900

4900

2436216

[{'FirstName': 'Chung Hyuk', 'LastName': 'Park', 'PI_MID_INIT': None, 'PI_SUFX_NAME': None, 'PI_FULL_NAME': 'Chung Hyuk Park', 'EmailAddress': 'chpark@email.gwu.edu', 'NSF_ID': '000703413', 'StartDate': '08/20/2024', 'EndDate': None, 'RoleCode': 'Co-Principal Investigator'}, {'FirstName': 'Huixia', 'LastName': 'Wang', 'PI_MID_INIT': 'J', 'PI_SUFX_NAME': None, 'PI_FULL_NAME': 'Huixia J Wang', 'EmailAddress': 'judywang@gwu.edu', 'NSF_ID': '000297034', 'StartDate': '08/20/2024', 'EndDate': None, 'RoleCode': 'Principal Investigator'}]

{'Name': 'George Washington University', 'CityName': 'WASHINGTON', 'ZipCode': '200520042', 'PhoneNumber': '2029940728', 'StreetAddress': '1918 F ST NW', 'StreetAddress2': None, 'CountryName': 'United States', 'StateName': 'District of Columbia', 'StateCode': 'DC', 'CONGRESSDISTRICT': '00', 'CONGRESS_DISTRICT_ORG': 'DC00', 'ORG_UEI_NUM': 'ECR5E2LU5BL6', 'ORG_LGL_BUS_NAME': 'GEORGE WASHINGTON UNIVERSITY (THE)', 'ORG_PRNT_UEI_NUM': None}

{'Name': 'George Washington University', 'CityName': 'WASHINGTON', 'StateCode': 'DC', 'ZipCode': '200520042', 'StreetAddress': '1918 F ST NW', 'CountryCode': 'US', 'CountryName': 'United States', 'StateName': 'District of Columbia', 'CountryFlag': '1', 'CONGRESSDISTRICT': '00', 'CONGRESS_DISTRICT_PERF': 'DC00'}

[{'Code': '125300', 'Text': 'OFFICE OF MULTIDISCIPLINARY AC'}, {'Code': '126900', 'Text': 'STATISTICS'}, {'Code': '534500', 'Text': 'Engineering of Biomed Systems'}, {'Code': '723100', 'Text': 'CYBERINFRASTRUCTURE'}, {'Code': 'Y18200', 'Text': None}]

2024~549341

{'url': 'https://www.nsf.gov/awardsearch/download?DownloadFileName=2024&All=true', 'xml': '2436216.xml'}

Collaborative Research: FDT-BioTech: Advancing Mathematical and Statistical Foundations to Enhance Human Digital Twin of Neurophysiological Modeling and Uncertainty Quantification

NSF

01/01/2025

12/31/2027

{'Value': 'Standard Grant'}

{'Code': '03040000', 'Directorate': {'Abbreviation': 'MPS', 'LongName': 'Direct For Mathematical & Physical Scien'}, 'Division': {'Abbreviation': 'DMS', 'LongName': 'Division Of Mathematical Sciences'}}

{'SignBlockName': 'Zhilan Feng', 'PO_EMAI': 'zfeng@nsf.gov', 'PO_PHON': '7032927523'}

This project aims to develop the mathematical foundations for a digital twin (DT) system for individuals with autism spectrum disorder (ASD), focusing on dynamic modeling, prediction, uncertainty quantification, and treatment or intervention recommendation through DT-based optimization. ASD is characterized by challenges in social interaction, communication, and behavior, such as difficulties in forming relationships, understanding nonverbal cues, speech development, repetitive behaviors, and sensory sensitivities. The project will create a unified system integrating clinical and neuro-developmental data, analyzed using a DT healthcare paradigm. The DT technology will enable individualized models, and its predictive capabilities will allow healthcare providers to anticipate progression and adjust treatment or intervention proactively. Additionally, the continuous feedback loop from real-time data will enhance therapeutic outcomes. The developed methods and theories will have broader applicability to other medical areas, improving healthcare efficiency, reducing system burdens, and informing public health strategies. This will ultimately enhance care and promote community well-being. The project will also develop quality cyberinfrastructure to share algorithms, data, and open-source software with the community. Furthermore, the investigators plan to expand scientific impacts through collaborating with medical experts and industry scientists, training undergraduate and graduate students, and integrating research findings into course development.<br/><br/>The project will develop a DT framework by modeling brain activities with a unified data structure, linked to behavioral characteristics and interventions aligned with individuals' neuro-developmental processes. This system will integrate multimodal and multi-source data related to human health and development. It will establish foundational models for training and generating synthetic data from DT models, enabling personalized predictions of progression and uncertainty quantification through novel interdisciplinary approaches. The DT system consists of four research modules: (1) Develop computational models based on conditional variational auto-encoders (CVAE) and longitudinal CVAE to analyze brain activities, integrate diverse imaging data, and model neurodevelopmental processes. (2) Create a novel bilevel formulation for multi-distribution fine-tuning techniques on pretrained foundational models and a fast algorithm to learn from heterogeneous data sources to predict ASD outcomes. (3) Develop a model-free conformal prediction procedure to ensemble predictions from multiple models obtained with different modalities and progression simulations, integrating various types of uncertainties into one framework. (4) Develop a DT-based reinforcement learning framework to recommend personalized treatment/intervention plans that significantly improve online learning efficiency and clinical outcomes. The project will address challenges such as multimodality and multi-source data, high-dimensional features, dynamic progression of ASD symptoms, brain functional connectivity, and the need for personalized intervention or treatment recommendations and uncertainty quantification.<br/><br/>This project is jointly funded by the Division of Mathematical Sciences, the OAC Cyberinfrastructure for Sustained Scientific Innovation (CSSI) program, and the CBET Engineering of Biomedical Systems program.<br/><br/>This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

08/20/2024

08/20/2024

None

Grant

47.049, 47.070

1

4900

4900

2436217

[{'FirstName': 'Mingrui', 'LastName': 'Liu', 'PI_MID_INIT': None, 'PI_SUFX_NAME': None, 'PI_FULL_NAME': 'Mingrui Liu', 'EmailAddress': 'mingruil@gmu.edu', 'NSF_ID': '000866854', 'StartDate': '08/20/2024', 'EndDate': None, 'RoleCode': 'Co-Principal Investigator'}, {'FirstName': 'Jie', 'LastName': 'Xu', 'PI_MID_INIT': None, 'PI_SUFX_NAME': None, 'PI_FULL_NAME': 'Jie Xu', 'EmailAddress': 'jxu13@gmu.edu', 'NSF_ID': '000616410', 'StartDate': '08/20/2024', 'EndDate': None, 'RoleCode': 'Principal Investigator'}]

{'Name': 'George Mason University', 'CityName': 'FAIRFAX', 'ZipCode': '220304422', 'PhoneNumber': '7039932295', 'StreetAddress': '4400 UNIVERSITY DR', 'StreetAddress2': None, 'CountryName': 'United States', 'StateName': 'Virginia', 'StateCode': 'VA', 'CONGRESSDISTRICT': '11', 'CONGRESS_DISTRICT_ORG': 'VA11', 'ORG_UEI_NUM': 'EADLFP7Z72E5', 'ORG_LGL_BUS_NAME': 'GEORGE MASON UNIVERSITY', 'ORG_PRNT_UEI_NUM': 'H4NRWLFCDF43'}

{'Name': 'George Mason University', 'CityName': 'FAIRFAX', 'StateCode': 'VA', 'ZipCode': '220304422', 'StreetAddress': '4400 UNIVERSITY DR', 'CountryCode': 'US', 'CountryName': 'United States', 'StateName': 'Virginia', 'CountryFlag': '1', 'CONGRESSDISTRICT': '11', 'CONGRESS_DISTRICT_PERF': 'VA11'}

[{'Code': '723100', 'Text': 'CYBERINFRASTRUCTURE'}, {'Code': 'Y18200', 'Text': None}]

2024~249971

{'url': 'https://www.nsf.gov/awardsearch/download?DownloadFileName=2024&All=true', 'xml': '2436217.xml'}

MPOPHC: A Novel Mean-Field Game Modeling Framework with Interdependent Health Policies and Public Opinions Feedback Loop for Real-Time Public Health Decision Support

NSF

01/01/2025

12/31/2027

{'Value': 'Standard Grant'}

{'Code': '03040000', 'Directorate': {'Abbreviation': 'MPS', 'LongName': 'Direct For Mathematical & Physical Scien'}, 'Division': {'Abbreviation': 'DMS', 'LongName': 'Division Of Mathematical Sciences'}}

{'SignBlockName': 'Zhilan Feng', 'PO_EMAI': 'zfeng@nsf.gov', 'PO_PHON': '7032927523'}

Mathematical models, particularly ones that characterize key epidemiological mechanisms such as transmission, enable public health policymakers to estimate epidemic risks, quantify uncertainties, and evaluate policy implications throughout epidemics. This project aims to address deficiencies in current mechanistic modeling paradigms by further integrating the often-neglected feedback loop among various public health policies (such as vaccination and non-pharmaceutical interventions), dynamic public opinions toward these policies during different phases of an epidemic, and critical outcomes such as hospitalization and death. This project establishes a detailed, integrated system encompassing policy, opinion, and epidemic dynamics, supported by robust mathematical methodologies and novel computational opinion mining approaches. This system will serve as a resource for developing, evaluating, and adjusting public health policies. The methodology developed can be applied to mechanistic models beyond the scope of this project, contributing to the broader field of mathematical epidemiology. Additionally, this project seeks to train the next generation of multidisciplinary modeling and public health teams, ensuring more precise situational awareness and policy support, ultimately enabling our society to stay ahead of the curve in future epidemics. <br/><br/>This project aims to develop and deliver innovative mathematical models for the co-evolution of public opinions and epidemic dynamics within the framework of mean field games (MFGs), resulting in an integrated system of epidemic MFG equations. The MFG approach captures the complex feedback among public health policies, dynamic public opinions, and epidemic outcomes that are not well captured by the commonly used susceptible-exposed-infected-recovered (SEIR)-type compartment and agent-based models. MFGs will significantly enhance our ability to track the coupled public opinion-epidemic system under spatially and temporally heterogeneous health policies. Additionally, this project will develop robust convexification numerical methods with guaranteed global convergence to accurately infer critical parameters (e.g., transmission coefficient, recovery rate, ...) from observed data, treating these as coefficient inverse problems. Furthermore, advanced natural language processing techniques, including content analysis and sentiment analysis, will be developed to characterize real-time public opinion and estimate compliance with various health policies across time and space. The integrated MFG system will be simulated under various scenarios, such as different public health policies and varying compliance, to predict future epidemic outcomes for policy decision support. <br/><br/>This award is jointly funded by the NSF Division of Mathematical Sciences (DMS) through the Mathematical Biology program and Division of Environment Biology (DEB). This project was also co-funded in collaboration with the CDC.<br/><br/>This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

08/23/2024

08/23/2024

None

Grant

47.049, 47.074

1

4900

4900

2436227

[{'FirstName': 'Michael', 'LastName': 'Klibanov', 'PI_MID_INIT': None, 'PI_SUFX_NAME': None, 'PI_FULL_NAME': 'Michael Klibanov', 'EmailAddress': 'mklibanv@uncc.edu', 'NSF_ID': '000136631', 'StartDate': '08/23/2024', 'EndDate': None, 'RoleCode': 'Co-Principal Investigator'}, {'FirstName': 'Daniel', 'LastName': 'Janies', 'PI_MID_INIT': 'A', 'PI_SUFX_NAME': None, 'PI_FULL_NAME': 'Daniel A Janies', 'EmailAddress': 'djanies@uncc.edu', 'NSF_ID': '000321997', 'StartDate': '08/23/2024', 'EndDate': None, 'RoleCode': 'Co-Principal Investigator'}, {'FirstName': 'Kevin', 'LastName': 'McGoff', 'PI_MID_INIT': None, 'PI_SUFX_NAME': None, 'PI_FULL_NAME': 'Kevin McGoff', 'EmailAddress': 'kmcgoff1@uncc.edu', 'NSF_ID': '000702146', 'StartDate': '08/23/2024', 'EndDate': None, 'RoleCode': 'Co-Principal Investigator'}, {'FirstName': 'Shi', 'LastName': 'Chen', 'PI_MID_INIT': None, 'PI_SUFX_NAME': None, 'PI_FULL_NAME': 'Shi Chen', 'EmailAddress': 'schen56@charlotte.edu', 'NSF_ID': '000996137', 'StartDate': '08/23/2024', 'EndDate': None, 'RoleCode': 'Principal Investigator'}, {'FirstName': 'Michael', 'LastName': 'Dulin', 'PI_MID_INIT': 'F', 'PI_SUFX_NAME': None, 'PI_FULL_NAME': 'Michael F Dulin', 'EmailAddress': 'mdulin3@uncc.edu', 'NSF_ID': '0000A0G5R', 'StartDate': '08/23/2024', 'EndDate': None, 'RoleCode': 'Co-Principal Investigator'}]

{'Name': 'University of North Carolina at Charlotte', 'CityName': 'CHARLOTTE', 'ZipCode': '282230001', 'PhoneNumber': '7046871888', 'StreetAddress': '9201 UNIVERSITY CITY BLVD', 'StreetAddress2': None, 'CountryName': 'United States', 'StateName': 'North Carolina', 'StateCode': 'NC', 'CONGRESSDISTRICT': '12', 'CONGRESS_DISTRICT_ORG': 'NC12', 'ORG_UEI_NUM': 'JB33DT84JNA5', 'ORG_LGL_BUS_NAME': 'UNIVERSITY OF NORTH CAROLINA AT CHARLOTTE, THE', 'ORG_PRNT_UEI_NUM': 'N8DMK1K4C2K5'}

{'Name': 'University of North Carolina at Charlotte', 'CityName': 'CHARLOTTE', 'StateCode': 'NC', 'ZipCode': '282230001', 'StreetAddress': '9201 UNIVERSITY CITY BLVD', 'CountryCode': 'US', 'CountryName': 'United States', 'StateName': 'North Carolina', 'CountryFlag': '1', 'CONGRESSDISTRICT': '12', 'CONGRESS_DISTRICT_PERF': 'NC12'}

[{'Code': '733400', 'Text': 'MATHEMATICAL BIOLOGY'}, {'Code': 'Y10400', 'Text': None}]

2024~535680

{'url': 'https://www.nsf.gov/awardsearch/download?DownloadFileName=2024&All=true', 'xml': '2436227.xml'}

Collaborative Research: MATH-DT Closing the generalization gap of digital twins

NSF

03/01/2025

02/29/2028

{'Value': 'Standard Grant'}

{'Code': '03040000', 'Directorate': {'Abbreviation': 'MPS', 'LongName': 'Direct For Mathematical & Physical Scien'}, 'Division': {'Abbreviation': 'DMS', 'LongName': 'Division Of Mathematical Sciences'}}

{'SignBlockName': 'Dmitry Golovaty', 'PO_EMAI': 'dgolovat@nsf.gov', 'PO_PHON': '7032922117'}

From the weather to human health to fighter jets, there are many complex systems whose outcomes we would like to predict and control. To achieve these goals, scientists and engineers often build digital twins---computer models that emulate and interact with the underlying physical systems. The current project describes fundamental research into the generalization ability of digital twins: to what degree can digital twins predict outcomes under conditions they have not previously encountered? For example, if the digital twin for an airplane has only seen data collected under normal operating conditions, can it accurately predict the plane's response to turbulence? By combining mathematical tools from nonlinear dynamics and computational tools from machine learning, this project aims to develop fundamental theories on generalization and build robust digital twins that can perform well in extreme or unexpected conditions. While the proposed framework applies to a broad class of complex systems, it is first being applied to circadian rhythms, which are the internal timekeeping mechanisms of the human body. Human biological clocks are increasingly subject to disturbances introduced by modern lifestyles such as long-haul air travel and nighttime computer use. Predictive digital twins can give personalized recommendations on effective interventions, such as optimal strategies to speed up recovery from jet lags. The project will also provide opportunities to teach modern mathematical concepts to a diverse population of undergraduate and graduate students. Through this project, students learn valuable skills in mathematical modeling, data analysis, science communication, and gain first-hand experience in building and managing state-of-the-art machine learning pipelines.<br/><br/>Current domain-agnostic digital twins based on deep neural networks are very expressive but can struggle when generalizing beyond their training conditions. Physics-based digital twins, on the other hand, generalize better to unseen conditions thanks to the strong inductive bias built into the model. On the other hand, they are often not sufficiently flexible to fully capture the rich dynamics in data. This project develops a new class of hybrid digital twins with tunable physics-based and domain-agnostic components, allowing practitioners to balance expressivity versus generalization, depending on the available data and the nature of the task. Utilizing concepts such as basins of attraction in multistable dynamical systems, a key objective of the project is to quantify how the generalization ability of the digital twin changes as the weights assigned to the two components are adjusted. In particular, the project explores the possibility that a properly weighted domain-agnostic component in the hybrid digital twin can sometimes improve out-of-distribution generalization, especially when the inductive bias provided by the physics-based component is imperfect. Digital twins that generalize to unseen conditions are crucial to applications such as finding optimal interventions for restoring disrupted circadian rhythms. For example, to find optimal strategies to speed up recovery from jet lags, a digital twin needs to predict the dynamics of a severely perturbed circadian clock based on data gathered mostly from normally operating clocks. These investigations will guide the creation of more robust digital twins and help inform critical decisions under new or uncertain conditions.<br/><br/>This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

08/28/2024

08/28/2024

None

Grant

47.049

1

4900

4900

2436231

{'FirstName': 'Yuanzhao', 'LastName': 'Zhang', 'PI_MID_INIT': None, 'PI_SUFX_NAME': None, 'PI_FULL_NAME': 'Yuanzhao Zhang', 'EmailAddress': 'yuanzhao.zhang.1@gmail.com', 'NSF_ID': '0000A09P0', 'StartDate': '08/28/2024', 'EndDate': None, 'RoleCode': 'Principal Investigator'}

{'Name': 'Santa Fe Institute', 'CityName': 'SANTA FE', 'ZipCode': '875018943', 'PhoneNumber': '5059462727', 'StreetAddress': '1399 HYDE PARK RD', 'StreetAddress2': None, 'CountryName': 'United States', 'StateName': 'New Mexico', 'StateCode': 'NM', 'CONGRESSDISTRICT': '03', 'CONGRESS_DISTRICT_ORG': 'NM03', 'ORG_UEI_NUM': 'M8SBQ7NVNAH4', 'ORG_LGL_BUS_NAME': 'SANTA FE INSTITUTE OF SCIENCE', 'ORG_PRNT_UEI_NUM': None}

{'Name': 'Santa Fe Institute', 'CityName': 'SANTA FE', 'StateCode': 'NM', 'ZipCode': '875018943', 'StreetAddress': '1399 HYDE PARK RD', 'CountryCode': 'US', 'CountryName': 'United States', 'StateName': 'New Mexico', 'CountryFlag': '1', 'CONGRESSDISTRICT': '03', 'CONGRESS_DISTRICT_PERF': 'NM03'}

[{'Code': '125300', 'Text': 'OFFICE OF MULTIDISCIPLINARY AC'}, {'Code': '127100', 'Text': 'COMPUTATIONAL MATHEMATICS'}]

2024~269979

{'url': 'https://www.nsf.gov/awardsearch/download?DownloadFileName=2024&All=true', 'xml': '2436231.xml'}

Collaborative Research: MATH-DT Closing the generalization gap of digital twins

NSF

03/01/2025

02/29/2028

{'Value': 'Standard Grant'}

{'Code': '03040000', 'Directorate': {'Abbreviation': 'MPS', 'LongName': 'Direct For Mathematical & Physical Scien'}, 'Division': {'Abbreviation': 'DMS', 'LongName': 'Division Of Mathematical Sciences'}}

{'SignBlockName': 'Dmitry Golovaty', 'PO_EMAI': 'dgolovat@nsf.gov', 'PO_PHON': '7032922117'}

From the weather to human health to fighter jets, there are many complex systems whose outcomes we would like to predict and control. To achieve these goals, scientists and engineers often build digital twins---computer models that emulate and interact with the underlying physical systems. The current project describes fundamental research into the generalization ability of digital twins: to what degree can digital twins predict outcomes under conditions they have not previously encountered? For example, if the digital twin for an airplane has only seen data collected under normal operating conditions, can it accurately predict the plane's response to turbulence? By combining mathematical tools from nonlinear dynamics and computational tools from machine learning, this project aims to develop fundamental theories on generalization and build robust digital twins that can perform well in extreme or unexpected conditions. While the proposed framework applies to a broad class of complex systems, it is first being applied to circadian rhythms, which are the internal timekeeping mechanisms of the human body. Human biological clocks are increasingly subject to disturbances introduced by modern lifestyles such as long-haul air travel and nighttime computer use. Predictive digital twins can give personalized recommendations on effective interventions, such as optimal strategies to speed up recovery from jet lags. The project will also provide opportunities to teach modern mathematical concepts to a diverse population of undergraduate and graduate students. Through this project, students learn valuable skills in mathematical modeling, data analysis, science communication, and gain first-hand experience in building and managing state-of-the-art machine learning pipelines.<br/><br/>Current domain-agnostic digital twins based on deep neural networks are very expressive but can struggle when generalizing beyond their training conditions. Physics-based digital twins, on the other hand, generalize better to unseen conditions thanks to the strong inductive bias built into the model. On the other hand, they are often not sufficiently flexible to fully capture the rich dynamics in data. This project develops a new class of hybrid digital twins with tunable physics-based and domain-agnostic components, allowing practitioners to balance expressivity versus generalization, depending on the available data and the nature of the task. Utilizing concepts such as basins of attraction in multistable dynamical systems, a key objective of the project is to quantify how the generalization ability of the digital twin changes as the weights assigned to the two components are adjusted. In particular, the project explores the possibility that a properly weighted domain-agnostic component in the hybrid digital twin can sometimes improve out-of-distribution generalization, especially when the inductive bias provided by the physics-based component is imperfect. Digital twins that generalize to unseen conditions are crucial to applications such as finding optimal interventions for restoring disrupted circadian rhythms. For example, to find optimal strategies to speed up recovery from jet lags, a digital twin needs to predict the dynamics of a severely perturbed circadian clock based on data gathered mostly from normally operating clocks. These investigations will guide the creation of more robust digital twins and help inform critical decisions under new or uncertain conditions.<br/><br/>This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

08/28/2024

08/28/2024

None

Grant

47.049

1

4900

4900

2436232

{'FirstName': 'Yitong', 'LastName': 'Huang', 'PI_MID_INIT': None, 'PI_SUFX_NAME': None, 'PI_FULL_NAME': 'Yitong Huang', 'EmailAddress': 'yhuang86@smith.edu', 'NSF_ID': '0000A0C4M', 'StartDate': '08/28/2024', 'EndDate': None, 'RoleCode': 'Principal Investigator'}

{'Name': 'Smith College', 'CityName': 'NORTHAMPTON', 'ZipCode': '010636304', 'PhoneNumber': '4135842700', 'StreetAddress': '10 ELM ST', 'StreetAddress2': None, 'CountryName': 'United States', 'StateName': 'Massachusetts', 'StateCode': 'MA', 'CONGRESSDISTRICT': '02', 'CONGRESS_DISTRICT_ORG': 'MA02', 'ORG_UEI_NUM': 'KRDJLRA9X6F3', 'ORG_LGL_BUS_NAME': 'THE TRUSTEES OF THE SMITH COLLEGE', 'ORG_PRNT_UEI_NUM': None}

{'Name': 'Smith College', 'CityName': 'NORTHAMPTON', 'StateCode': 'MA', 'ZipCode': '010636304', 'StreetAddress': '10 ELM ST', 'CountryCode': 'US', 'CountryName': 'United States', 'StateName': 'Massachusetts', 'CountryFlag': '1', 'CONGRESSDISTRICT': '02', 'CONGRESS_DISTRICT_PERF': 'MA02'}

[{'Code': '125300', 'Text': 'OFFICE OF MULTIDISCIPLINARY AC'}, {'Code': '127100', 'Text': 'COMPUTATIONAL MATHEMATICS'}]

2024~269187

{'url': 'https://www.nsf.gov/awardsearch/download?DownloadFileName=2024&All=true', 'xml': '2436232.xml'}

Collaborative Research: MATH-DT Closing the generalization gap of digital twins

NSF

03/01/2025

02/29/2028

{'Value': 'Standard Grant'}

{'Code': '03040000', 'Directorate': {'Abbreviation': 'MPS', 'LongName': 'Direct For Mathematical & Physical Scien'}, 'Division': {'Abbreviation': 'DMS', 'LongName': 'Division Of Mathematical Sciences'}}

{'SignBlockName': 'Dmitry Golovaty', 'PO_EMAI': 'dgolovat@nsf.gov', 'PO_PHON': '7032922117'}

From the weather to human health to fighter jets, there are many complex systems whose outcomes we would like to predict and control. To achieve these goals, scientists and engineers often build digital twins---computer models that emulate and interact with the underlying physical systems. The current project describes fundamental research into the generalization ability of digital twins: to what degree can digital twins predict outcomes under conditions they have not previously encountered? For example, if the digital twin for an airplane has only seen data collected under normal operating conditions, can it accurately predict the plane's response to turbulence? By combining mathematical tools from nonlinear dynamics and computational tools from machine learning, this project aims to develop fundamental theories on generalization and build robust digital twins that can perform well in extreme or unexpected conditions. While the proposed framework applies to a broad class of complex systems, it is first being applied to circadian rhythms, which are the internal timekeeping mechanisms of the human body. Human biological clocks are increasingly subject to disturbances introduced by modern lifestyles such as long-haul air travel and nighttime computer use. Predictive digital twins can give personalized recommendations on effective interventions, such as optimal strategies to speed up recovery from jet lags. The project will also provide opportunities to teach modern mathematical concepts to a diverse population of undergraduate and graduate students. Through this project, students learn valuable skills in mathematical modeling, data analysis, science communication, and gain first-hand experience in building and managing state-of-the-art machine learning pipelines.<br/><br/>Current domain-agnostic digital twins based on deep neural networks are very expressive but can struggle when generalizing beyond their training conditions. Physics-based digital twins, on the other hand, generalize better to unseen conditions thanks to the strong inductive bias built into the model. On the other hand, they are often not sufficiently flexible to fully capture the rich dynamics in data. This project develops a new class of hybrid digital twins with tunable physics-based and domain-agnostic components, allowing practitioners to balance expressivity versus generalization, depending on the available data and the nature of the task. Utilizing concepts such as basins of attraction in multistable dynamical systems, a key objective of the project is to quantify how the generalization ability of the digital twin changes as the weights assigned to the two components are adjusted. In particular, the project explores the possibility that a properly weighted domain-agnostic component in the hybrid digital twin can sometimes improve out-of-distribution generalization, especially when the inductive bias provided by the physics-based component is imperfect. Digital twins that generalize to unseen conditions are crucial to applications such as finding optimal interventions for restoring disrupted circadian rhythms. For example, to find optimal strategies to speed up recovery from jet lags, a digital twin needs to predict the dynamics of a severely perturbed circadian clock based on data gathered mostly from normally operating clocks. These investigations will guide the creation of more robust digital twins and help inform critical decisions under new or uncertain conditions.<br/><br/>This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

08/28/2024

08/28/2024

None

Grant

47.049

1

4900

4900

2436233

{'FirstName': 'William', 'LastName': 'Gilpin', 'PI_MID_INIT': None, 'PI_SUFX_NAME': None, 'PI_FULL_NAME': 'William Gilpin', 'EmailAddress': 'wgilpin@utexas.edu', 'NSF_ID': '000786817', 'StartDate': '08/28/2024', 'EndDate': None, 'RoleCode': 'Principal Investigator'}

{'Name': 'University of Texas at Austin', 'CityName': 'AUSTIN', 'ZipCode': '787121139', 'PhoneNumber': '5124716424', 'StreetAddress': '110 INNER CAMPUS DR', 'StreetAddress2': None, 'CountryName': 'United States', 'StateName': 'Texas', 'StateCode': 'TX', 'CONGRESSDISTRICT': '25', 'CONGRESS_DISTRICT_ORG': 'TX25', 'ORG_UEI_NUM': 'V6AFQPN18437', 'ORG_LGL_BUS_NAME': 'UNIVERSITY OF TEXAS AT AUSTIN', 'ORG_PRNT_UEI_NUM': None}

{'Name': 'University of Texas at Austin', 'CityName': 'AUSTIN', 'StateCode': 'TX', 'ZipCode': '787121139', 'StreetAddress': '110 INNER CAMPUS DR', 'CountryCode': 'US', 'CountryName': 'United States', 'StateName': 'Texas', 'CountryFlag': '1', 'CONGRESSDISTRICT': '25', 'CONGRESS_DISTRICT_PERF': 'TX25'}

[{'Code': '125300', 'Text': 'OFFICE OF MULTIDISCIPLINARY AC'}, {'Code': '127100', 'Text': 'COMPUTATIONAL MATHEMATICS'}]

2024~250000

{'url': 'https://www.nsf.gov/awardsearch/download?DownloadFileName=2024&All=true', 'xml': '2436233.xml'}

Participation in the City: How Urban Participatory Innovations are Reshaping Democracy, Governance and Trust

NSF

09/15/2024

08/31/2027

{'Value': 'Standard Grant'}

{'Code': '04010000', 'Directorate': {'Abbreviation': 'SBE', 'LongName': 'Direct For Social, Behav & Economic Scie'}, 'Division': {'Abbreviation': 'SMA', 'LongName': 'SBE Off Of Multidisciplinary Activities'}}

{'SignBlockName': 'Antwan Jones', 'PO_EMAI': 'aajones@nsf.gov', 'PO_PHON': '7032924973'}

This award will fund a research project to study democratic innovations taking place in urban areas. Cities have been a key source of myriad urban participatory innovations (UPIs) that create new practices and institutions that allow citizens to inform and reshape democracy. Urban participatory innovations include both grassroots attempts to use physical and digital spaces to build trust and reshape democracy, as well as institutional reforms such as open government and participatory design of institutions even as they are also sites of political conflict and deep inequalities. This project (PAR-CITY) will examine how and why cities are responding to the democratic challenges by better understanding urban participatory institutions in seven cities. It builds on existing research and incorporates new empirical work using a variety of research methods, including qualitative and quantitative analyses. The research will shift disciplinary landscapes by centering the role of cities and UPIs in studies of democracy, governance, and trust (DGT), drawing new relations between disciplines and geographical contexts, producing a co-authored book, several journal articles and a digital platform.<br/><br/>An interdisciplinary set of 25 researchers working in three work streams will undertake a relational comparison of the seven cities to address three central research questions: 1) How are urban participatory innovations (UPIs) reshaping power, authority, and conflict? 2) How do UPIs confront marginalization and inequalities? And 3) How do concepts, understandings, and practices of UPIs relate across geographical differences? By exploring these questions, the team will achieve several goals, such as establishing the empirical significance of cities for responding to the global challenges of democracy, governance and trust. PAR-CITY examines how UPIs enhance democratic processes, improve governance and rebuild trust in cities. PAR-CITY will provide comparative and interdisciplinary data that help understand the significance of cities to the central themes of this T-AP call, beyond very localized and often outdated studies. PAR-CITY will also examine the role of digital media, tools and technologies in democracy, governance and trust in large cities. In particular PAR-CITY will examine UPIs through smart cities, social media, open government and the use of digital technology in participatory governance. Digital innovations have the potential to break down distance between citizens and institutions yet can also present negative externalities that exacerbate inequality, marginalization and conflict. PAR-CITY will also advance concepts, models and theories of DGT through the central notion of UPI.<br/><br/>This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

08/15/2024

08/15/2024

None

Grant

47.075

1

4900

4900

2436273

{'FirstName': 'Stephanie', 'LastName': 'McNulty', 'PI_MID_INIT': None, 'PI_SUFX_NAME': None, 'PI_FULL_NAME': 'Stephanie McNulty', 'EmailAddress': 'smcnulty@fandm.edu', 'NSF_ID': '000710118', 'StartDate': '08/15/2024', 'EndDate': None, 'RoleCode': 'Principal Investigator'}

{'Name': 'Franklin and Marshall College', 'CityName': 'LANCASTER', 'ZipCode': '176032827', 'PhoneNumber': '7173584517', 'StreetAddress': '415 HARRISBURG AVE', 'StreetAddress2': None, 'CountryName': 'United States', 'StateName': 'Pennsylvania', 'StateCode': 'PA', 'CONGRESSDISTRICT': '11', 'CONGRESS_DISTRICT_ORG': 'PA11', 'ORG_UEI_NUM': 'P4NXVGAJNQK3', 'ORG_LGL_BUS_NAME': 'FRANKLIN AND MARSHALL COLLEGE', 'ORG_PRNT_UEI_NUM': 'P4NXVGAJNQK3'}

{'Name': 'Franklin and Marshall College', 'CityName': 'LANCASTER', 'StateCode': 'PA', 'ZipCode': '176032827', 'StreetAddress': '415 HARRISBURG AVE', 'CountryCode': 'US', 'CountryName': 'United States', 'StateName': 'Pennsylvania', 'CountryFlag': '1', 'CONGRESSDISTRICT': '11', 'CONGRESS_DISTRICT_PERF': 'PA11'}

{'Code': '188Y00', 'Text': 'T-AP-Trans-Atlantic Platform'}

2024~199060

{'url': 'https://www.nsf.gov/awardsearch/download?DownloadFileName=2024&All=true', 'xml': '2436273.xml'}

Collaborative Research: Direct Lending in the U.S. Middle Market

NSF

10/01/2023

07/31/2025

{'Value': 'Standard Grant'}

{'Code': '04050000', 'Directorate': {'Abbreviation': 'SBE', 'LongName': 'Direct For Social, Behav & Economic Scie'}, 'Division': {'Abbreviation': 'SES', 'LongName': 'Divn Of Social and Economic Sciences'}}

{'SignBlockName': 'Nancy Lutz', 'PO_EMAI': 'nlutz@nsf.gov', 'PO_PHON': '7032927280'}

Abstract<br/>The tightening of regulation in the U.S. banking sector following the Financial Crisis of 2008 contributed to a surge of alternative nonbank lenders and, in particular, business development companies (BDCs). This sector has expanded rapidly over the last two decades. This project will construct an extensive database and conduct the first systematic analysis of the BDC sector. The project will focus on these new lenders and provide insights into an important yet understudied segment of the U.S. economy - the middle market, which accounts for a third of private-sector employment. Specifically, the project will investigate the impact of nonbank lending on middle-market firms and economic growth. Overall, the project will have important policy implications for the role of financial intermediation in the economy.<br/><br/>By constructing a novel database, this project will advance the body of knowledge on nonbank lending. First, the project will investigate the causes of the BDC sector growth and analyze their role in financing middle-market firms. The granularity of the dataset will additionally allow to characterize the borrowers targeted and lending solutions offered. Second, the project will use the location information of BDC portfolio companies to estimate the real effects of these direct lenders on middle-market firms and, more broadly, on local economic growth. Third, the project will investigate how access to capital markets affects investment activities of direct lenders. Finally, since BDCs provide funding to firms without relying on deposit insurance, they put forward a new lending model that may offer an improvement to the current deposit-based banking system. To understand the effects of this alternative lending mechanism, the project will develop a quantitative framework to conduct a welfare analysis of transitioning to a new lending environment.<br/><br/>This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

06/21/2024

06/21/2024

None

Grant

47.075

1

4900

4900

2436298

{'FirstName': 'Tetiana', 'LastName': 'Davydiuk', 'PI_MID_INIT': None, 'PI_SUFX_NAME': None, 'PI_FULL_NAME': 'Tetiana Davydiuk', 'EmailAddress': 'tetianad@andrew.cmu.edu', 'NSF_ID': '000807743', 'StartDate': '06/21/2024', 'EndDate': None, 'RoleCode': 'Principal Investigator'}

{'Name': 'Johns Hopkins University', 'CityName': 'BALTIMORE', 'ZipCode': '212182608', 'PhoneNumber': '4439971898', 'StreetAddress': '3400 N CHARLES ST', 'StreetAddress2': None, 'CountryName': 'United States', 'StateName': 'Maryland', 'StateCode': 'MD', 'CONGRESSDISTRICT': '07', 'CONGRESS_DISTRICT_ORG': 'MD07', 'ORG_UEI_NUM': 'FTMTDMBR29C7', 'ORG_LGL_BUS_NAME': 'THE JOHNS HOPKINS UNIVERSITY', 'ORG_PRNT_UEI_NUM': None}

{'Name': 'Johns Hopkins University', 'CityName': 'BALTIMORE', 'StateCode': 'MD', 'ZipCode': '212182608', 'StreetAddress': '3400 N CHARLES ST', 'CountryCode': 'US', 'CountryName': 'United States', 'StateName': 'Maryland', 'CountryFlag': '1', 'CONGRESSDISTRICT': '07', 'CONGRESS_DISTRICT_PERF': 'MD07'}

{'Code': '132000', 'Text': 'Economics'}

2020~84112

{'url': 'https://www.nsf.gov/awardsearch/download?DownloadFileName=2024&All=true', 'xml': '2436298.xml'}

Governance, Trust, and Engagement in Past and Present

NSF

09/15/2024

08/31/2026

{'Value': 'Standard Grant'}

{'Code': '04010000', 'Directorate': {'Abbreviation': 'SBE', 'LongName': 'Direct For Social, Behav & Economic Scie'}, 'Division': {'Abbreviation': 'SMA', 'LongName': 'SBE Off Of Multidisciplinary Activities'}}

{'SignBlockName': 'Antwan Jones', 'PO_EMAI': 'aajones@nsf.gov', 'PO_PHON': '7032924973'}

This collaborative research will study how organizations strengthen governance. It will trace the efforts of organizations that have sought to resolve international crises from the end of World War I to contemporary times. The research will draw on history, international relations, sociology, and political science to study the dynamics of organizations to understand how institutions sustain governance and trust around the world. It will study how domestic politics have shaped nations’ attitudes toward these bodies. The project will also study how non-governmental organizations (NGOs) have tried to use these organizations to achieve their objectives. The results of this research will provide inputs into policies regarding international organizations to make governance work better for future generations.<br/> <br/>The interdisciplinary project team will pursue two lines of inquiry. Specifically, it will examine campaigns that have sought to create, reform, transform, or abolish international organizations. This will highlight the potentials and shortfalls of organizations. It will also investigate attempts of NGOs to enlist the support of international bodies in response to obstacles they encounter domestically. These findings will draw on the past using historical methodologies to strengthen understanding of how future cooperation can be reimagined and will advance fundamental science in sociology and political science. The team will produce a co-authored monograph and engage stakeholders through papers and workshops.<br/><br/>This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

08/27/2024

08/27/2024

None

Grant

47.075

1

4900

4900

2436313

{'FirstName': 'Susan', 'LastName': 'Stokes', 'PI_MID_INIT': 'C', 'PI_SUFX_NAME': None, 'PI_FULL_NAME': 'Susan C Stokes', 'EmailAddress': 'sstokes@uchicago.edu', 'NSF_ID': '000318767', 'StartDate': '08/27/2024', 'EndDate': None, 'RoleCode': 'Principal Investigator'}

{'Name': 'University of Chicago', 'CityName': 'CHICAGO', 'ZipCode': '606375418', 'PhoneNumber': '7737028669', 'StreetAddress': '5801 S ELLIS AVE', 'StreetAddress2': None, 'CountryName': 'United States', 'StateName': 'Illinois', 'StateCode': 'IL', 'CONGRESSDISTRICT': '01', 'CONGRESS_DISTRICT_ORG': 'IL01', 'ORG_UEI_NUM': 'ZUE9HKT2CLC9', 'ORG_LGL_BUS_NAME': 'UNIVERSITY OF CHICAGO', 'ORG_PRNT_UEI_NUM': 'ZUE9HKT2CLC9'}

{'Name': 'University of Chicago', 'CityName': 'CHICAGO', 'StateCode': 'IL', 'ZipCode': '606371580', 'StreetAddress': '5801 S ELLIS AVE', 'CountryCode': 'US', 'CountryName': 'United States', 'StateName': 'Illinois', 'CountryFlag': '1', 'CONGRESSDISTRICT': '01', 'CONGRESS_DISTRICT_PERF': 'IL01'}

{'Code': '188Y00', 'Text': 'T-AP-Trans-Atlantic Platform'}

2024~200000

{'url': 'https://www.nsf.gov/awardsearch/download?DownloadFileName=2024&All=true', 'xml': '2436313.xml'}

Collaborative Research: MATH-DT: Mathematical Foundations of Quantum Digital Twins

NSF

09/01/2024

08/31/2027

{'Value': 'Standard Grant'}

{'Code': '03040000', 'Directorate': {'Abbreviation': 'MPS', 'LongName': 'Direct For Mathematical & Physical Scien'}, 'Division': {'Abbreviation': 'DMS', 'LongName': 'Division Of Mathematical Sciences'}}

{'SignBlockName': 'Jodi Mead', 'PO_EMAI': 'jmead@nsf.gov', 'PO_PHON': '7032927212'}

This project develops, analyzes, and deploys Quantum Digital Twins (QDTs), which are digital clones of existing quantum computers. Built within a comprehensive mathematical and statistical framework, these QDTs will enable bidirectional interactions between quantum computers and virtual models on classical systems, optimizing quantum performance and marking a significant step toward achieving the proverbial Quantum Leap in computational abilities. This advancement will help maintain the United States' leadership in quantum information science and technology, supporting the National Quantum Initiative Act and producing next-generation quantum-enabled technologies for sensing, information processing, communication, security, and computing. Additionally, the project establishes foundations that can enhance other Digital Twin technologies across various fields, from energy to health. It will also facilitate the interdisciplinary training of young scientists in modern data-driven computational methods and the experimental and theoretical aspects of quantum devices and digital twins, with outreach efforts to local communities and Native American tertiary colleges.<br/><br/>The QDTs developed in this project aim to overcome the limitations of traditional quantum simulations, which use a linear component-by-component approach, by introducing four key advancements: (i) the first-ever mathematical formulation of QDTs grounded in a Bayesian probabilistic framework, addressing the inherently probabilistic nature of quantum devices, (ii) new randomized Bayesian experimental design techniques tailored for QDTs, capable of handling the complex dynamics and uncertainties in quantum systems, (iii) a robust generalized Bayesian framework using optimal transportation theory with adaptive prior and model enrichment mechanisms, enabling QDTs to detect and correct their flaws while minimizing system downtime, and (iv) advanced risk-neutral techniques for quantum optimal control and validation, improving QDTs' ability to generate high-fidelity quantum gates. The project also integrates these algorithms and methods into existing open-source software products, demonstrating and disseminating the developed QDTs.<br/><br/>This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

08/09/2024

08/09/2024

None

Grant

47.049

1

4900

4900

2436318

[{'FirstName': 'Gabriel', 'LastName': 'Huerta', 'PI_MID_INIT': None, 'PI_SUFX_NAME': None, 'PI_FULL_NAME': 'Gabriel Huerta', 'EmailAddress': 'ghuerta@unm.edu', 'NSF_ID': '000253366', 'StartDate': '08/09/2024', 'EndDate': None, 'RoleCode': 'Co-Principal Investigator'}, {'FirstName': 'Mohammad', 'LastName': 'Motamed', 'PI_MID_INIT': None, 'PI_SUFX_NAME': None, 'PI_FULL_NAME': 'Mohammad Motamed', 'EmailAddress': 'motamed@math.unm.edu', 'NSF_ID': '000660841', 'StartDate': '08/09/2024', 'EndDate': None, 'RoleCode': 'Principal Investigator'}]

{'Name': 'University of New Mexico', 'CityName': 'ALBUQUERQUE', 'ZipCode': '87131', 'PhoneNumber': '5052774186', 'StreetAddress': '1700 LOMAS BLVD NE STE 2200', 'StreetAddress2': None, 'CountryName': 'United States', 'StateName': 'New Mexico', 'StateCode': 'NM', 'CONGRESSDISTRICT': '01', 'CONGRESS_DISTRICT_ORG': 'NM01', 'ORG_UEI_NUM': 'F6XLTRUQJEN4', 'ORG_LGL_BUS_NAME': 'UNIVERSITY OF NEW MEXICO', 'ORG_PRNT_UEI_NUM': None}

{'Name': 'University of New Mexico', 'CityName': 'ALBUQUERQUE', 'StateCode': 'NM', 'ZipCode': '871310001', 'StreetAddress': '1700 LOMAS BLVD NE STE 2200', 'CountryCode': 'US', 'CountryName': 'United States', 'StateName': 'New Mexico', 'CountryFlag': '1', 'CONGRESSDISTRICT': '01', 'CONGRESS_DISTRICT_PERF': 'NM01'}

[{'Code': '125300', 'Text': 'OFFICE OF MULTIDISCIPLINARY AC'}, {'Code': '127100', 'Text': 'COMPUTATIONAL MATHEMATICS'}]

2024~299990

{'url': 'https://www.nsf.gov/awardsearch/download?DownloadFileName=2024&All=true', 'xml': '2436318.xml'}

Collaborative Research: MATH-DT: Mathematical Foundations of Quantum Digital Twins

NSF

09/01/2024

08/31/2027

{'Value': 'Standard Grant'}

{'Code': '03040000', 'Directorate': {'Abbreviation': 'MPS', 'LongName': 'Direct For Mathematical & Physical Scien'}, 'Division': {'Abbreviation': 'DMS', 'LongName': 'Division Of Mathematical Sciences'}}

{'SignBlockName': 'Jodi Mead', 'PO_EMAI': 'jmead@nsf.gov', 'PO_PHON': '7032927212'}

This project develops, analyzes, and deploys Quantum Digital Twins (QDTs), which are digital clones of existing quantum computers. Built within a comprehensive mathematical and statistical framework, these QDTs will enable bidirectional interactions between quantum computers and virtual models on classical systems, optimizing quantum performance and marking a significant step toward achieving the proverbial Quantum Leap in computational abilities. This advancement will help maintain the United States' leadership in quantum information science and technology, supporting the National Quantum Initiative Act and producing next-generation quantum-enabled technologies for sensing, information processing, communication, security, and computing. Additionally, the project establishes foundations that can enhance other Digital Twin technologies across various fields, from energy to health. It will also facilitate the interdisciplinary training of young scientists in modern data-driven computational methods and the experimental and theoretical aspects of quantum devices and digital twins, with outreach efforts to local communities and Native American tertiary colleges.<br/><br/>The QDTs developed in this project aim to overcome the limitations of traditional quantum simulations, which use a linear component-by-component approach, by introducing four key advancements: (i) the first-ever mathematical formulation of QDTs grounded in a Bayesian probabilistic framework, addressing the inherently probabilistic nature of quantum devices, (ii) new randomized Bayesian experimental design techniques tailored for QDTs, capable of handling the complex dynamics and uncertainties in quantum systems, (iii) a robust generalized Bayesian framework using optimal transportation theory with adaptive prior and model enrichment mechanisms, enabling QDTs to detect and correct their flaws while minimizing system downtime, and (iv) advanced risk-neutral techniques for quantum optimal control and validation, improving QDTs' ability to generate high-fidelity quantum gates. The project also integrates these algorithms and methods into existing open-source software products, demonstrating and disseminating the developed QDTs.<br/><br/>This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

08/09/2024

08/09/2024

None

Grant

47.049

1

4900

4900

2436319

[{'FirstName': 'Xinwei', 'LastName': 'Deng', 'PI_MID_INIT': None, 'PI_SUFX_NAME': None, 'PI_FULL_NAME': 'Xinwei Deng', 'EmailAddress': 'xdeng@vt.edu', 'NSF_ID': '000600120', 'StartDate': '08/09/2024', 'EndDate': None, 'RoleCode': 'Co-Principal Investigator'}, {'FirstName': 'Daniel', 'LastName': 'Appelo', 'PI_MID_INIT': None, 'PI_SUFX_NAME': None, 'PI_FULL_NAME': 'Daniel Appelo', 'EmailAddress': 'appelo@vt.edu', 'NSF_ID': '000611476', 'StartDate': '08/09/2024', 'EndDate': None, 'RoleCode': 'Principal Investigator'}]

{'Name': 'Virginia Polytechnic Institute and State University', 'CityName': 'BLACKSBURG', 'ZipCode': '240603359', 'PhoneNumber': '5402315281', 'StreetAddress': '300 TURNER ST NW', 'StreetAddress2': 'STE 4200', 'CountryName': 'United States', 'StateName': 'Virginia', 'StateCode': 'VA', 'CONGRESSDISTRICT': '09', 'CONGRESS_DISTRICT_ORG': 'VA09', 'ORG_UEI_NUM': 'QDE5UHE5XD16', 'ORG_LGL_BUS_NAME': 'VIRGINIA POLYTECHNIC INSTITUTE & STATE UNIVERSITY', 'ORG_PRNT_UEI_NUM': 'M515A1DKXAN8'}

{'Name': 'Virginia Polytechnic Institute and State University', 'CityName': 'BLACKSBURG', 'StateCode': 'VA', 'ZipCode': '240603359', 'StreetAddress': '300 TURNER ST NW', 'CountryCode': 'US', 'CountryName': 'United States', 'StateName': 'Virginia', 'CountryFlag': '1', 'CONGRESSDISTRICT': '09', 'CONGRESS_DISTRICT_PERF': 'VA09'}

[{'Code': '125300', 'Text': 'OFFICE OF MULTIDISCIPLINARY AC'}, {'Code': '127100', 'Text': 'COMPUTATIONAL MATHEMATICS'}]

2024~299148

{'url': 'https://www.nsf.gov/awardsearch/download?DownloadFileName=2024&All=true', 'xml': '2436319.xml'}

MPOPHC: Incorporation of Game Theory Tools to Improve the Policy Making to Mitigate Epidemics of Respiratory Diseases

NSF

01/01/2025

12/31/2027

{'Value': 'Standard Grant'}

{'Code': '03040000', 'Directorate': {'Abbreviation': 'MPS', 'LongName': 'Direct For Mathematical & Physical Scien'}, 'Division': {'Abbreviation': 'DMS', 'LongName': 'Division Of Mathematical Sciences'}}

{'SignBlockName': 'Zhilan Feng', 'PO_EMAI': 'zfeng@nsf.gov', 'PO_PHON': '7032927523'}

During the COVID-19 pandemic, it was observed that individuals did not always follow mitigation policies closely. Instead, they behaved according to their own objectives, where demographic and socioeconomic factors seemed to have influenced their responses to the set policies. Therefore, this project aims to improve the policymaking processes to mitigate the transmission of respiratory pathogens by incorporating the individuals’ decision-making and socio-demographic heterogeneities. To do this, the investigators propose to develop and study game theoretical mathematical models, as well as simulation tools and numerical approaches that can be adapted to specific public health problems of interest to practitioners and researchers. These tools will be made publicly available. This project will also involve interdisciplinary training for graduate students in applied mathematics, statistics, operations research, epidemiology, and quantitative biology.<br/><br/>To model many interacting agents, the investigators will develop and study extensions of mean field games (MFGs). First, they will focus on building multi-population MFGs and graphon games to incorporate socio-demographic heterogeneities while finding the Nash equilibrium responses of individuals under different disease mitigation policies (e.g., vaccination policies and non-pharmaceutical interventions). Furthermore, different equilibrium notions to incorporate altruism in the populations will be explored through the introduction of mixed multi-population MFGs that include both cooperative and non-cooperative individuals. Later, the investigators will focus on finding optimal mitigation policies by using Stackelberg MFGs that include the optimization of a regulator (e.g., a governmental institution). The extensions of Stackelberg MFGs that include heterogeneities in the mean field populations, altruistic behaviors, and possible state variables for the regulator will be developed and analyzed. Surveys and analyses of publicly available data will be conducted to calibrate and parameterize the mathematical models to capture real-life patterns. Finally, numerical approaches and simulation toolboxes will be implemented to solve large dimensional and more complex models, which will allow policymakers to adapt and parametrize our models according to their specific needs. <br/><br/>This award is co-funded by the NSF Division of Mathematical Sciences (DMS) and the CDC Coronavirus and Other Respiratory Viruses Division (CORVD).<br/><br/>This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

08/20/2024

08/20/2024

None

Grant

47.049

1

4900

4900

2436332

[{'FirstName': 'Gokce', 'LastName': 'Dayanikli', 'PI_MID_INIT': None, 'PI_SUFX_NAME': None, 'PI_FULL_NAME': 'Gokce Dayanikli', 'EmailAddress': 'gokced@illinois.edu', 'NSF_ID': '000939521', 'StartDate': '08/20/2024', 'EndDate': None, 'RoleCode': 'Principal Investigator'}, {'FirstName': 'Pamela', 'LastName': 'Martinez', 'PI_MID_INIT': 'P', 'PI_SUFX_NAME': None, 'PI_FULL_NAME': 'Pamela P Martinez', 'EmailAddress': 'pamelapm@illinois.edu', 'NSF_ID': '000837126', 'StartDate': '08/20/2024', 'EndDate': None, 'RoleCode': 'Co-Principal Investigator'}]

{'Name': 'University of Illinois at Urbana-Champaign', 'CityName': 'URBANA', 'ZipCode': '618013620', 'PhoneNumber': '2173332187', 'StreetAddress': '506 S WRIGHT ST', 'StreetAddress2': None, 'CountryName': 'United States', 'StateName': 'Illinois', 'StateCode': 'IL', 'CONGRESSDISTRICT': '13', 'CONGRESS_DISTRICT_ORG': 'IL13', 'ORG_UEI_NUM': 'Y8CWNJRCNN91', 'ORG_LGL_BUS_NAME': 'UNIVERSITY OF ILLINOIS', 'ORG_PRNT_UEI_NUM': None}

{'Name': 'University of Illinois at Urbana-Champaign', 'CityName': 'Champaign', 'StateCode': 'IL', 'ZipCode': '618205710', 'StreetAddress': '605 E Springfield Ave', 'CountryCode': 'US', 'CountryName': 'United States', 'StateName': 'Illinois', 'CountryFlag': '1', 'CONGRESSDISTRICT': '13', 'CONGRESS_DISTRICT_PERF': 'IL13'}

[{'Code': '733400', 'Text': 'MATHEMATICAL BIOLOGY'}, {'Code': 'Y20600', 'Text': None}]

2024~360000

{'url': 'https://www.nsf.gov/awardsearch/download?DownloadFileName=2024&All=true', 'xml': '2436332.xml'}

Math-DT: Advancing the Mathematical Foundations for Dynamic Digital Twinning of Next-Generation Mobile Wireless Networks

NSF

01/01/2025

12/31/2027

{'Value': 'Standard Grant'}

{'Code': '03040000', 'Directorate': {'Abbreviation': 'MPS', 'LongName': 'Direct For Mathematical & Physical Scien'}, 'Division': {'Abbreviation': 'DMS', 'LongName': 'Division Of Mathematical Sciences'}}

{'SignBlockName': 'Dmitry Golovaty', 'PO_EMAI': 'dgolovat@nsf.gov', 'PO_PHON': '7032922117'}

Digital Twins–virtual models of physical systems–have garnered growing attention in recent years, driven by rapid advances in sensing, communications, computing, machine learning and artificial intelligence, and hold the potential to vastly accelerate scientific discovery and revolutionize many industries. In particular, Digital Twins play a fundamental role in designing, managing and optimizing 5G wireless networks and will be essential in enabling next-generation 6G wireless networks. Despite recent advances in realistic channel modeling, there are still many fundamental challenges in developing fit-for-purpose Digital Twins for next generation wireless networks that can seamlessly integrate data for informed decision making, and can be dynamically updated as the physical environment varies and network operational objectives change. Motivated by these challenges, the team of investigators develops novel mathematical theories, and new data-driven, AI-guided models and algorithms that will lay the mathematical foundations for digital twinning of next generation wireless networks. The award also supports undergraduate and graduate students from underrepresented groups in research and educational activities as well as organization of K-12 outreach programs.<br/><br/>This proposal aims to advance the mathematical foundations of next generation wireless network Digital Twins. The investigators will place the ray tracing problem–essential to such digital twins–in the more general framework of first order Hamilton-Jacobi equations, and will make theoretical and algorithmic advances in data-driven learning of Hamilton-Jacobi equations. The research team will prove optimal sample size complexity bounds for learning Hamilton-Jacobi equations and their solutions from data, and develop algorithms for achieving these bounds, in both the static and active learning settings. They will develop a temporal surface reconstruction algorithm that combines temporal LiDAR and video camera information by leveraging neural kernels and transport equations. In order to quantify the uncertainty in their results, the investigators will establish posterior contraction rates for learning Hamilton-Jacobi equations, and develop methods to construct and analyze Bayesian credible sets and perform scalable posterior sampling. Finally, the investigators will integrate their theoretical and algorithmic advances into a next generation wireless network Digital Twin platform that will be evaluated in both controlled and dynamic real-world environments.<br/><br/>This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

08/09/2024

08/09/2024

None

Grant

47.049

1

4900

4900

2436333

[{'FirstName': 'Zhi-Li', 'LastName': 'Zhang', 'PI_MID_INIT': None, 'PI_SUFX_NAME': None, 'PI_FULL_NAME': 'Zhi-Li Zhang', 'EmailAddress': 'zhzhang@cs.umn.edu', 'NSF_ID': '000096921', 'StartDate': '08/09/2024', 'EndDate': None, 'RoleCode': 'Co-Principal Investigator'}, {'FirstName': 'Jeffrey', 'LastName': 'Calder', 'PI_MID_INIT': None, 'PI_SUFX_NAME': None, 'PI_FULL_NAME': 'Jeffrey Calder', 'EmailAddress': 'jwcalder@umn.edu', 'NSF_ID': '000677566', 'StartDate': '08/09/2024', 'EndDate': None, 'RoleCode': 'Principal Investigator'}, {'FirstName': 'Yulong', 'LastName': 'Lu', 'PI_MID_INIT': None, 'PI_SUFX_NAME': None, 'PI_FULL_NAME': 'Yulong Lu', 'EmailAddress': 'lu000683@umn.edu', 'NSF_ID': '000842262', 'StartDate': '08/09/2024', 'EndDate': None, 'RoleCode': 'Co-Principal Investigator'}]

{'Name': 'University of Minnesota-Twin Cities', 'CityName': 'MINNEAPOLIS', 'ZipCode': '554552009', 'PhoneNumber': '6126245599', 'StreetAddress': '200 OAK ST SE', 'StreetAddress2': None, 'CountryName': 'United States', 'StateName': 'Minnesota', 'StateCode': 'MN', 'CONGRESSDISTRICT': '05', 'CONGRESS_DISTRICT_ORG': 'MN05', 'ORG_UEI_NUM': 'KABJZBBJ4B54', 'ORG_LGL_BUS_NAME': 'REGENTS OF THE UNIVERSITY OF MINNESOTA', 'ORG_PRNT_UEI_NUM': None}

{'Name': 'University of Minnesota-Twin Cities', 'CityName': 'MINNEAPOLIS', 'StateCode': 'MN', 'ZipCode': '554550488', 'StreetAddress': '206 Church St. SE', 'CountryCode': 'US', 'CountryName': 'United States', 'StateName': 'Minnesota', 'CountryFlag': '1', 'CONGRESSDISTRICT': '05', 'CONGRESS_DISTRICT_PERF': 'MN05'}

[{'Code': '125300', 'Text': 'OFFICE OF MULTIDISCIPLINARY AC'}, {'Code': '127100', 'Text': 'COMPUTATIONAL MATHEMATICS'}]

2024~900000

{'url': 'https://www.nsf.gov/awardsearch/download?DownloadFileName=2024&All=true', 'xml': '2436333.xml'}

MPOPHC: Quantitative design of effective testing-based policies through infection trajectory modeling

NSF

01/01/2025

12/31/2027

{'Value': 'Standard Grant'}

{'Code': '03040000', 'Directorate': {'Abbreviation': 'MPS', 'LongName': 'Direct For Mathematical & Physical Scien'}, 'Division': {'Abbreviation': 'DMS', 'LongName': 'Division Of Mathematical Sciences'}}

{'SignBlockName': 'Zhilan Feng', 'PO_EMAI': 'zfeng@nsf.gov', 'PO_PHON': '7032927523'}

Diagnostic tests play a crucial role in the management of infectious disease transmission. Testing is the fastest most reliable way to inform a person whether they are infected, and thus whether they should adjust their behavior to prevent onward spread. Testing policies have long contributed to public health, including in the control of HIV, tuberculosis, and malaria. During the COVID-19 pandemic, various test-based policies were successful, including pre-event screening (e.g., testing before entering a sporting event), traveler screening (e.g., testing before boarding a flight), and regular screening (e.g., weekly testing at universities). Such policies could also help control the spread of other existing and novel respiratory pathogens. However, we currently lack a robust, data-driven framework to estimate the potential impact of testing-based infection control strategies in general. To fill this gap, this project will develop a flexible modeling framework to simulate how different testing policies might perform for various pathogens, tests, and human behavioral scenarios. This project will also develop the statistical tools needed to infer how diagnostic test results, infectiousness, and behavior relate to one another, informed by data on SARS-CoV-2 and other respiratory pathogens. To maximize the impact of these findings, this project will build mature, open-source software products to compare testing-based policies, accompanied by tutorials for policymakers and a new open-source data hub to consolidate information relevant to testing-based policies. The successful completion of this project will improve our ability to control existing respiratory pathogens and enhance our preparedness for future pandemics. <br/><br/>Fundamental to this project is the characterization of how infectiousness, detectability, symptoms, and behaviors change over the course of a respiratory infection – a collection of features called an infection trajectory. While the details of an infection trajectory can be omitted for some types of policy assessments, testing-based policies depend critically on an accurate and statistical understanding of infection trajectories. Infection trajectory-based models allow for the separation of individual-level features of disease transmission from the between-host dynamics, permitting a “plug-and-play” approach to policy design, without compromising the ability to tailor solutions to local needs and populations. This project’s policy modeling framework will develop a stochastic description of infection trajectories, represented by a joint distribution of an infection’s measurable variables. This will allow the researchers to assess variability in policy outcomes and to identify cross-policy interactions. This project will develop a framework to infer infection trajectory distributions from multimodal data and will deploy that framework to guide the design of studies for collecting new infection trajectory data. Finally, this project will create a suite of software, educational, and data tools for informing infection trajectories and associated policies. For the public health policy community, successful completion of this project will produce new, high-quality policy design models and assessment tools, complemented by educational and interactive exploration webpages. For the scientific community, this project will provide statistical tools and data sharing standards for infection trajectory data, supporting advances in virology and modeling. This award is co-funded by the NSF Division of Mathematical Sciences (DMS) and the CDC Coronavirus and Other Respiratory Viruses Division (CORVD).<br/><br/>This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

08/08/2024

08/08/2024

None

Grant

47.049

1

4900

4900

2436340

[{'FirstName': 'Daniel', 'LastName': 'Larremore', 'PI_MID_INIT': 'B', 'PI_SUFX_NAME': None, 'PI_FULL_NAME': 'Daniel B Larremore', 'EmailAddress': 'daniel.larremore@colorado.edu', 'NSF_ID': '000605019', 'StartDate': '08/08/2024', 'EndDate': None, 'RoleCode': 'Co-Principal Investigator'}, {'FirstName': 'Stephen', 'LastName': 'Kissler', 'PI_MID_INIT': None, 'PI_SUFX_NAME': None, 'PI_FULL_NAME': 'Stephen Kissler', 'EmailAddress': 'stephen.kissler@gmail.com', 'NSF_ID': '000878487', 'StartDate': '08/08/2024', 'EndDate': None, 'RoleCode': 'Principal Investigator'}]

{'Name': 'University of Colorado at Boulder', 'CityName': 'Boulder', 'ZipCode': '803090001', 'PhoneNumber': '3034926221', 'StreetAddress': '3100 MARINE ST', 'StreetAddress2': 'STE 481 572 UCB', 'CountryName': 'United States', 'StateName': 'Colorado', 'StateCode': 'CO', 'CONGRESSDISTRICT': '02', 'CONGRESS_DISTRICT_ORG': 'CO02', 'ORG_UEI_NUM': 'SPVKK1RC2MZ3', 'ORG_LGL_BUS_NAME': 'THE REGENTS OF THE UNIVERSITY OF COLORADO', 'ORG_PRNT_UEI_NUM': None}

{'Name': 'University of Colorado at Boulder', 'CityName': 'Boulder', 'StateCode': 'CO', 'ZipCode': '803090001', 'StreetAddress': '3100 MARINE ST', 'CountryCode': 'US', 'CountryName': 'United States', 'StateName': 'Colorado', 'CountryFlag': '1', 'CONGRESSDISTRICT': '02', 'CONGRESS_DISTRICT_PERF': 'CO02'}

[{'Code': '733400', 'Text': 'MATHEMATICAL BIOLOGY'}, {'Code': 'Y20600', 'Text': None}]

2024~968765

{'url': 'https://www.nsf.gov/awardsearch/download?DownloadFileName=2024&All=true', 'xml': '2436340.xml'}

Collaborative Research: MATH-DT: Mathematical Foundations of AI-assisted Digital Twins for High Power Laser Science and Engineering

NSF

10/01/2024

09/30/2027

{'Value': 'Standard Grant'}

{'Code': '03040000', 'Directorate': {'Abbreviation': 'MPS', 'LongName': 'Direct For Mathematical & Physical Scien'}, 'Division': {'Abbreviation': 'DMS', 'LongName': 'Division Of Mathematical Sciences'}}

{'SignBlockName': 'Troy D. Butler', 'PO_EMAI': 'tdbutler@nsf.gov', 'PO_PHON': '7032922084'}

Laser technology is one of the most transformative inventions of the modern era, which has become an indispensable tool for scientific research and technological innovation - revolutionizing the semiconductor industry, telecommunications, healthcare, and defense. However, current laser design and manufacturing approaches remain stagnant, stymieing further breakthroughs. Developing novel integrated systems of laser architectures, components, and techniques leveraging digital twins (DT) is imperative to expand frontiers in intensity, wavelength regime, and high average power. This project will fill this gap using state-of-the-art predictive and generative artificial intelligence (AI) coupled with physical principles and high-fidelity, close-loop, rapid feedback between digital models and physical systems. Graduate students and postdoctoral researchers will also be integrated within the research team as part of the training of the next generation of scientists required to advance the field. <br/><br/>This project will develop theoretical foundations for AI-assisted DTs to integrate scientific data, physical models, and machine learning for complex high-power laser science and engineering (HPLSE) to enable efficient design, failure and performance prediction, operational optimization, and emerging lasing conditions. Laser technologies are extremely complex to model because they rely on a cascaded set of mode-locked laser dynamics and a manifold of architectures and configurations of chirped pulse amplification, and nonlinear optical stages, such as parametric amplification. Their architectural complexity and multi-dimensional data far exceed current modeling and analysis tools. The project will address these challenges by (1) extracting reduced representation of scientific data from experiments or high-fidelity HPLSE simulation, (2) building data-efficient and physics-aware predictive machine learning surrogate models of laser fields with uncertainty quantification, and (3) developing generative model-based rapid closed-loop control between digital models and physical high-power laser systems. The project will be AI-focused, multi-disciplinary, and involve a diverse workforce of future scientists and engineers. The project will also include an education thrust to integrate the research results into interdisciplinary education. The project will bolster AI foundations and its application curricula at both UCLA and the University of Utah. More critically, it will forge a robust collaboration among mathematics, data science, and laser technologies.<br/><br/>This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

08/09/2024

08/09/2024

None

Grant

47.049

1

4900

4900

2436343

[{'FirstName': 'Andrea', 'LastName': 'Bertozzi', 'PI_MID_INIT': 'L', 'PI_SUFX_NAME': None, 'PI_FULL_NAME': 'Andrea L Bertozzi', 'EmailAddress': 'bertozzi@math.ucla.edu', 'NSF_ID': '000107633', 'StartDate': '08/09/2024', 'EndDate': None, 'RoleCode': 'Principal Investigator'}, {'FirstName': 'Sergio', 'LastName': 'Carbajo', 'PI_MID_INIT': None, 'PI_SUFX_NAME': None, 'PI_FULL_NAME': 'Sergio Carbajo', 'EmailAddress': 'scarbajo@ucla.edu', 'NSF_ID': '000855974', 'StartDate': '08/09/2024', 'EndDate': None, 'RoleCode': 'Co-Principal Investigator'}]

{'Name': 'University of California-Los Angeles', 'CityName': 'LOS ANGELES', 'ZipCode': '900244200', 'PhoneNumber': '3107940102', 'StreetAddress': '10889 WILSHIRE BLVD STE 700', 'StreetAddress2': None, 'CountryName': 'United States', 'StateName': 'California', 'StateCode': 'CA', 'CONGRESSDISTRICT': '36', 'CONGRESS_DISTRICT_ORG': 'CA36', 'ORG_UEI_NUM': 'RN64EPNH8JC6', 'ORG_LGL_BUS_NAME': 'UNIVERSITY OF CALIFORNIA, LOS ANGELES', 'ORG_PRNT_UEI_NUM': None}

{'Name': 'University of California-Los Angeles', 'CityName': 'LOS ANGELES', 'StateCode': 'CA', 'ZipCode': '900957227', 'StreetAddress': '570 Westwood Plaza', 'CountryCode': 'US', 'CountryName': 'United States', 'StateName': 'California', 'CountryFlag': '1', 'CONGRESSDISTRICT': '36', 'CONGRESS_DISTRICT_PERF': 'CA36'}

[{'Code': '125300', 'Text': 'OFFICE OF MULTIDISCIPLINARY AC'}, {'Code': '127100', 'Text': 'COMPUTATIONAL MATHEMATICS'}]

2024~569051

{'url': 'https://www.nsf.gov/awardsearch/download?DownloadFileName=2024&All=true', 'xml': '2436343.xml'}

Collaborative Research: MATH-DT: Mathematical Foundations of AI-assisted Digital Twins for High Power Laser Science and Engineering

NSF

10/01/2024

09/30/2027

{'Value': 'Standard Grant'}

{'Code': '03040000', 'Directorate': {'Abbreviation': 'MPS', 'LongName': 'Direct For Mathematical & Physical Scien'}, 'Division': {'Abbreviation': 'DMS', 'LongName': 'Division Of Mathematical Sciences'}}

{'SignBlockName': 'Troy D. Butler', 'PO_EMAI': 'tdbutler@nsf.gov', 'PO_PHON': '7032922084'}

Laser technology is one of the most transformative inventions of the modern era, which has become an indispensable tool for scientific research and technological innovation - revolutionizing the semiconductor industry, telecommunications, healthcare, and defense. However, current laser design and manufacturing approaches remain stagnant, stymieing further breakthroughs. Developing novel integrated systems of laser architectures, components, and techniques leveraging digital twins (DT) is imperative to expand frontiers in intensity, wavelength regime, and high average power. This project will fill this gap using state-of-the-art predictive and generative artificial intelligence (AI) coupled with physical principles and high-fidelity, close-loop, rapid feedback between digital models and physical systems. Graduate students and postdoctoral researchers will also be integrated within the research team as part of the training of the next generation of scientists required to advance the field. <br/><br/>This project will develop theoretical foundations for AI-assisted DTs to integrate scientific data, physical models, and machine learning for complex high-power laser science and engineering (HPLSE) to enable efficient design, failure and performance prediction, operational optimization, and emerging lasing conditions. Laser technologies are extremely complex to model because they rely on a cascaded set of mode-locked laser dynamics and a manifold of architectures and configurations of chirped pulse amplification, and nonlinear optical stages, such as parametric amplification. Their architectural complexity and multi-dimensional data far exceed current modeling and analysis tools. The project will address these challenges by (1) extracting reduced representation of scientific data from experiments or high-fidelity HPLSE simulation, (2) building data-efficient and physics-aware predictive machine learning surrogate models of laser fields with uncertainty quantification, and (3) developing generative model-based rapid closed-loop control between digital models and physical high-power laser systems. The project will be AI-focused, multi-disciplinary, and involve a diverse workforce of future scientists and engineers. The project will also include an education thrust to integrate the research results into interdisciplinary education. The project will bolster AI foundations and its application curricula at both UCLA and the University of Utah. More critically, it will forge a robust collaboration among mathematics, data science, and laser technologies.<br/><br/>This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

08/09/2024

08/09/2024

None

Grant

47.049

1

4900

4900

2436344

{'FirstName': 'Bao', 'LastName': 'Wang', 'PI_MID_INIT': None, 'PI_SUFX_NAME': None, 'PI_FULL_NAME': 'Bao Wang', 'EmailAddress': 'bwang@math.utah.edu', 'NSF_ID': '000797442', 'StartDate': '08/09/2024', 'EndDate': None, 'RoleCode': 'Principal Investigator'}

{'Name': 'University of Utah', 'CityName': 'SALT LAKE CITY', 'ZipCode': '841129049', 'PhoneNumber': '8015816903', 'StreetAddress': '201 PRESIDENTS CIR', 'StreetAddress2': None, 'CountryName': 'United States', 'StateName': 'Utah', 'StateCode': 'UT', 'CONGRESSDISTRICT': '01', 'CONGRESS_DISTRICT_ORG': 'UT01', 'ORG_UEI_NUM': 'LL8GLEVH6MG3', 'ORG_LGL_BUS_NAME': 'UNIVERSITY OF UTAH', 'ORG_PRNT_UEI_NUM': None}

{'Name': 'University of Utah', 'CityName': 'SALT LAKE CITY', 'StateCode': 'UT', 'ZipCode': '841129200', 'StreetAddress': '201 PRESIDENTS CIR', 'CountryCode': 'US', 'CountryName': 'United States', 'StateName': 'Utah', 'CountryFlag': '1', 'CONGRESSDISTRICT': '01', 'CONGRESS_DISTRICT_PERF': 'UT01'}

[{'Code': '125300', 'Text': 'OFFICE OF MULTIDISCIPLINARY AC'}, {'Code': '127100', 'Text': 'COMPUTATIONAL MATHEMATICS'}]

2024~250000

{'url': 'https://www.nsf.gov/awardsearch/download?DownloadFileName=2024&All=true', 'xml': '2436344.xml'}

MATH-DT: Advancing Digital Twins for Jet Engines Through Mathematical and Computational Innovation

NSF

09/01/2024

08/31/2027

{'Value': 'Standard Grant'}

{'Code': '03040000', 'Directorate': {'Abbreviation': 'MPS', 'LongName': 'Direct For Mathematical & Physical Scien'}, 'Division': {'Abbreviation': 'DMS', 'LongName': 'Division Of Mathematical Sciences'}}

{'SignBlockName': 'Jodi Mead', 'PO_EMAI': 'jmead@nsf.gov', 'PO_PHON': '7032927212'}

This project advances predictive digital twins of jet engines. The digital twin is a virtual object representing the jet engine and will be used to inform decisions such as design, optimization, operation and maintenance. This is crucial to enhancing aircraft safety whereby the digital twin enables a proactive approach to identifying and resolving potential issues, and to scheduling preventive maintenance. The digital twin allows a better understanding of the physical behaviors that an engine would exhibit under many operational scenarios, some too dangerous for physical experimentation. The research directly engages undergraduate students through teaching laboratories and a more in-depth engagement program targeted toward students from underrepresented and under-served groups in engineering. The project also involves training of doctoral students in Computer Science, Mathematics, and Engineering. <br/><br/>The overarching goal of this project is to expand the mathematical foundations of digital twins with application to jet engines, and to increase their predictive simulation capabilities by fusing information from advanced modeling and state-of-the-art measurements. It develops high-fidelity multiphysics models for jet engine combustion and flow, as well as scalable reduced-order models, both with quantified uncertainties. Particle-surface interaction models are constructed to quantify erosion and deposition effects on engine performance. An array of novel hierarchical data assimilation algorithms are developed using variational approaches, ensemble Kalman filters, and transport map particle filters, all in the context of a hierarchy of models. An innovative experimental setting at the Virginia Tech Advanced Propulsion and Power Laboratory with a JetCatP100-RX engine allows testing with the physical twin.<br/><br/>This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

08/09/2024

08/09/2024

None

Grant

47.049

1

4900

4900

2436357

[{'FirstName': 'Adrian', 'LastName': 'Sandu', 'PI_MID_INIT': None, 'PI_SUFX_NAME': None, 'PI_FULL_NAME': 'Adrian Sandu', 'EmailAddress': 'sandu@cs.vt.edu', 'NSF_ID': '000388914', 'StartDate': '08/09/2024', 'EndDate': None, 'RoleCode': 'Principal Investigator'}, {'FirstName': 'Rui', 'LastName': 'Qiao', 'PI_MID_INIT': None, 'PI_SUFX_NAME': None, 'PI_FULL_NAME': 'Rui Qiao', 'EmailAddress': 'ruiqiao@vt.edu', 'NSF_ID': '000234529', 'StartDate': '08/09/2024', 'EndDate': None, 'RoleCode': 'Co-Principal Investigator'}, {'FirstName': 'Christopher', 'LastName': 'Roy', 'PI_MID_INIT': 'J', 'PI_SUFX_NAME': None, 'PI_FULL_NAME': 'Christopher J Roy', 'EmailAddress': 'cjroy@vt.edu', 'NSF_ID': '000554886', 'StartDate': '08/09/2024', 'EndDate': None, 'RoleCode': 'Co-Principal Investigator'}, {'FirstName': 'Kevin', 'LastName': 'Lowe', 'PI_MID_INIT': 'T', 'PI_SUFX_NAME': None, 'PI_FULL_NAME': 'Kevin T Lowe', 'EmailAddress': 'kelowe@vt.edu', 'NSF_ID': '000589849', 'StartDate': '08/09/2024', 'EndDate': None, 'RoleCode': 'Co-Principal Investigator'}, {'FirstName': 'Ionut', 'LastName': 'Farcas', 'PI_MID_INIT': None, 'PI_SUFX_NAME': None, 'PI_FULL_NAME': 'Ionut Farcas', 'EmailAddress': 'farcasi@vt.edu', 'NSF_ID': '0000A07TX', 'StartDate': '08/09/2024', 'EndDate': None, 'RoleCode': 'Co-Principal Investigator'}]

{'Name': 'Virginia Polytechnic Institute and State University', 'CityName': 'BLACKSBURG', 'ZipCode': '240603359', 'PhoneNumber': '5402315281', 'StreetAddress': '300 TURNER ST NW', 'StreetAddress2': 'STE 4200', 'CountryName': 'United States', 'StateName': 'Virginia', 'StateCode': 'VA', 'CONGRESSDISTRICT': '09', 'CONGRESS_DISTRICT_ORG': 'VA09', 'ORG_UEI_NUM': 'QDE5UHE5XD16', 'ORG_LGL_BUS_NAME': 'VIRGINIA POLYTECHNIC INSTITUTE & STATE UNIVERSITY', 'ORG_PRNT_UEI_NUM': 'M515A1DKXAN8'}

{'Name': 'Virginia Polytechnic Institute and State University', 'CityName': 'BLACKSBURG', 'StateCode': 'VA', 'ZipCode': '240611050', 'StreetAddress': '620 Drillfield Drive', 'CountryCode': 'US', 'CountryName': 'United States', 'StateName': 'Virginia', 'CountryFlag': '1', 'CONGRESSDISTRICT': '09', 'CONGRESS_DISTRICT_PERF': 'VA09'}

[{'Code': '125300', 'Text': 'OFFICE OF MULTIDISCIPLINARY AC'}, {'Code': '127100', 'Text': 'COMPUTATIONAL MATHEMATICS'}]

2024~769412

{'url': 'https://www.nsf.gov/awardsearch/download?DownloadFileName=2024&All=true', 'xml': '2436357.xml'}

I-Corps: Translation potential of a microfluidic device to improve gene editing of therapeutic cells

NSF

09/01/2024

08/31/2025

{'Value': 'Standard Grant'}

{'Code': '15030000', 'Directorate': {'Abbreviation': 'TIP', 'LongName': 'Dir for Tech, Innovation, & Partnerships'}, 'Division': {'Abbreviation': 'TI', 'LongName': 'Translational Impacts'}}

{'SignBlockName': 'Ruth Shuman', 'PO_EMAI': 'rshuman@nsf.gov', 'PO_PHON': '7032922160'}

The broader impact of this I-Corps project is the development of a biotechnology tool to increase gene editing efficiency and accelerate the development of cell therapies. Gene editing technology, a method for making specific changes to the DNA of a cell, is used to turn human cells into therapeutic cells. The method may be used as a potential treatment and cure for many diseases including cancer. Currently, however, this application is limited by the low efficiency of gene editing, which results in only a few percent of the cells being engineered successfully and becoming therapeutic cells. This low yield makes cell therapy one of the most expensive treatments and creates significant unmet patient demand. With increased gene editing efficiency, more therapeutic cells may be created, which may lower the manufacturing cost of therapeutic cells and help cell therapies treat more patients.<br/><br/>This I-Corps project utilizes experiential learning coupled with a first-hand investigation of the industry ecosystem to assess the translation potential of a microfluidic device to increase gene editing efficiency. To achieve successful gene editing, materials used for gene editing need to be delivered into cells and have access to the target genes. However, many genes are densely packed and hidden within the chromatin, which makes them difficult to reach and leads to low editing efficiency. This technology tackles this issue through a mechanism called cell massage. Cells are gently squeezed through microchannels within the device, and the mechanical stimulation on the cell nucleus opens the chromatin structure temporarily. This opening allows the genes to be more accessible to gene editing materials. This solution has been shown to lead to a 10-fold increase in gene editing efficiency. The increased efficiency may lower the epigenetic barrier and make it easier for gene editing tools to reach and edit target genes.<br/><br/>This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

07/29/2024

07/29/2024

None

Grant

47.084

1

4900

4900

2436358

{'FirstName': 'Song', 'LastName': 'Li', 'PI_MID_INIT': None, 'PI_SUFX_NAME': None, 'PI_FULL_NAME': 'Song Li', 'EmailAddress': 'songli@ucla.edu', 'NSF_ID': '000186103', 'StartDate': '07/29/2024', 'EndDate': None, 'RoleCode': 'Principal Investigator'}

{'Name': 'University of California-Los Angeles', 'CityName': 'LOS ANGELES', 'ZipCode': '900244200', 'PhoneNumber': '3107940102', 'StreetAddress': '10889 WILSHIRE BLVD STE 700', 'StreetAddress2': None, 'CountryName': 'United States', 'StateName': 'California', 'StateCode': 'CA', 'CONGRESSDISTRICT': '36', 'CONGRESS_DISTRICT_ORG': 'CA36', 'ORG_UEI_NUM': 'RN64EPNH8JC6', 'ORG_LGL_BUS_NAME': 'UNIVERSITY OF CALIFORNIA, LOS ANGELES', 'ORG_PRNT_UEI_NUM': None}

{'Name': 'University of California-Los Angeles', 'CityName': 'LOS ANGELES', 'StateCode': 'CA', 'ZipCode': '900951600', 'StreetAddress': '410 Westwood Plaza', 'CountryCode': 'US', 'CountryName': 'United States', 'StateName': 'California', 'CountryFlag': '1', 'CONGRESSDISTRICT': '36', 'CONGRESS_DISTRICT_PERF': 'CA36'}

{'Code': '802300', 'Text': 'I-Corps'}

2024~50000

{'url': 'https://www.nsf.gov/awardsearch/download?DownloadFileName=2024&All=true', 'xml': '2436358.xml'}

Planning: Eastern North and South Carolina STEM Teacher Corps Alliance Planning Meeting

NSF

09/01/2024

02/28/2025

{'Value': 'Standard Grant'}

{'Code': '11040000', 'Directorate': {'Abbreviation': 'EDU', 'LongName': 'Directorate for STEM Education'}, 'Division': {'Abbreviation': 'DUE', 'LongName': 'Division Of Undergraduate Education'}}

{'SignBlockName': 'Patrice Waller', 'PO_EMAI': 'pwaller@nsf.gov', 'PO_PHON': '7032924944'}

This project aims to serve the national interest by forming a collaborative team – spanning multiple universities, K-12 districts, state-level education organizations, and non-profit entities across the predominantly rural Eastern Region of North and South Carolina – to recognize excellence and support professional leadership and growth opportunities for STEM teachers from traditional and alternative licensure pathways. This project supports a planning meeting with the team to develop a network that will recognize, elevate, and leverage the expertise of outstanding STEM teachers to promote professional learning for teachers from traditional and alternative licensure pathways throughout the region. The network will meet a significant need for more highly qualified STEM teachers, which will translate into high-level, equitable, and accessible learning opportunities for each student. Additionally, this project will provide an example of how to create a network of teacher leaders to support high-quality STEM instruction for not only Eastern North and South Carolina, but also with a focus on scalability. <br/><br/>The scope of this project is to identify potential members of the regional collaborative team and hold a two day in person planning meeting with dedicated time to 1) listen to regional stakeholders potentially served by this project, 2) understand the unique needs across contexts, 3) examine existing resources, programs, and infrastructure, and 4) synthesize knowledge and understanding gained to co-construct the vision, mission, goals, and objectives for the Eastern North Carolina and South Carolina STEM Teacher Corps Alliance. An additional outcome of the meeting will be an outline of a project proposal to be submitted to NSF National STEM Teacher Corps Pilot Program. This project's design will advance understanding of how a network, in which outstanding STEM teachers work with a collaborative team, can elevate STEM teachers, collectively, throughout a region. The NSF National STEM Teacher Corps Pilot Program supports the elevation of the STEM teacher profession by selecting and recognizing outstanding STEM educators that advance equity in our Nation’s PreK –12 classrooms and providing professional development of STEM teachers.<br/><br/>This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

08/22/2024

08/22/2024

None

Grant

47.076

1

4900

4900

2436424

[{'FirstName': 'Catherine', 'LastName': 'Schwartz', 'PI_MID_INIT': 'S', 'PI_SUFX_NAME': None, 'PI_FULL_NAME': 'Catherine S Schwartz', 'EmailAddress': 'schwartzca@ecu.edu', 'NSF_ID': '000544390', 'StartDate': '08/22/2024', 'EndDate': None, 'RoleCode': 'Co-Principal Investigator'}, {'FirstName': 'Charity', 'LastName': 'Cayton', 'PI_MID_INIT': None, 'PI_SUFX_NAME': None, 'PI_FULL_NAME': 'Charity Cayton', 'EmailAddress': 'caytonc@ecu.edu', 'NSF_ID': '000694031', 'StartDate': '08/22/2024', 'EndDate': None, 'RoleCode': 'Principal Investigator'}]

{'Name': 'East Carolina University', 'CityName': 'GREENVILLE', 'ZipCode': '278582502', 'PhoneNumber': '2523289530', 'StreetAddress': '1000 E 5TH ST', 'StreetAddress2': None, 'CountryName': 'United States', 'StateName': 'North Carolina', 'StateCode': 'NC', 'CONGRESSDISTRICT': '01', 'CONGRESS_DISTRICT_ORG': 'NC01', 'ORG_UEI_NUM': 'HWPEKM8VFTJ9', 'ORG_LGL_BUS_NAME': 'EAST CAROLINA UNIVERSITY', 'ORG_PRNT_UEI_NUM': None}

{'Name': 'East Carolina University', 'CityName': 'Greenville', 'StateCode': 'NC', 'ZipCode': '278581821', 'StreetAddress': '209 E 5th Street', 'CountryCode': 'US', 'CountryName': 'United States', 'StateName': 'North Carolina', 'CountryFlag': '1', 'CONGRESSDISTRICT': '01', 'CONGRESS_DISTRICT_PERF': 'NC01'}

{'Code': '311Y00', 'Text': 'National STEM Teacher Corps'}

2024~49697

{'url': 'https://www.nsf.gov/awardsearch/download?DownloadFileName=2024&All=true', 'xml': '2436424.xml'}

Reviewer Zero: Changing the Culture of Peer Review to Increase Diversity, Equity, and Inclusion

NSF

07/01/2024

09/30/2025

{'Value': 'Standard Grant'}

{'Code': '11010000', 'Directorate': {'Abbreviation': 'EDU', 'LongName': 'Directorate for STEM Education'}, 'Division': {'Abbreviation': 'DGE', 'LongName': 'Division Of Graduate Education'}}

{'SignBlockName': 'Kathleen Ehm', 'PO_EMAI': 'kehm@nsf.gov', 'PO_PHON': '7032925032'}

Science advances because scientists collect data, develop methods, and generate theories that become part of a shared scientific record. To be part of this shared record, scientific works go through peer review by other scientists. Although peer review is intended to promote rigorous standards, it also has consequences for the scientific workforce - for who wants to stay and who is able to stay, in research-focused careers. Despite peer review’s place as a core scientific practice, learning how to engage with peer review is not explicitly taught. Few people receive training or oversight to ensure that reviewers provide feedback that is helpful, professional, and culturally sensitive (i.e., delivered in a way that does not marginalize underrepresented minority scholars). Graduate students’ experiences with peer review can influence whether they decide to stay in the STEM pathway. This project examines peer review with an eye to equity (are outcomes and processes equitable across groups), inclusion (does peer review offer experiences of fit and belonging across groups), and diversity (does peer review contribute to increasing the range of identities and experiences constituting the field). This NSF Innovations of Graduate Education (IGE) award to Indiana University, Columbia University, and California State San Bernardino seeks to foster diversity, equity, and inclusion within science by improving peer review culture and graduate students’ ability to navigate peer review.<br/> <br/>This project supports the innovative structure and goals of Reviewer Zero, a coalition of faculty and graduate students in psychology and neuroscience working to understand and intervene to increase equity in peer review processes. Reviewer Zero envisions a “reset” of peer review culture in which reviews serve a formative rather than gatekeeping function. This project will develop strategic programming with two audiences: the historically underrepresented graduate students most directly affected by inequitable systems of peer review, and the reviewers/editors who occupy positions of power in making peer review decisions. The project will design, deliver, and assess interventions that build awareness, knowledge, and support for each audience. Specifically, the project asks how targeting different aspects of the culture cycle can best shift peer review culture toward greater equity. By re-imagining ideas about what peer review is, the evidence-based training will engage individuals with new tools and supports, whether they are trainees or reviewers. A new paper development system (Formative And Interactive Review) will provide a novel institutional structure for fundamentally different interactions between reviewers and trainees. Outreach and partnerships with existing institutions (journals, societies) will lead to the dissemination of new views of the goals and processes of peer review. Towards these goals, this project will implement a comprehensive strategy to increase diversity, equity, and inclusion in the peer review process by (a) working with reviewers/editors to shift culture and (b) providing direct support and training to graduate students navigating peer review. By studying how engaging with program activities affects trainee or reviewer/editor knowledge, skills, and abilities, the project will contribute to understanding how shifts in culture cycles occur.<br/><br/>The Innovations in Graduate Education (IGE) program is focused on research in graduate education. The goals of IGE are to pilot, test and validate innovative approaches to graduate education and to generate the knowledge required to move these approaches into the broader community.<br/><br/>This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

07/09/2024

07/09/2024

None

Grant

47.076

1

4900

4900

2436430

{'FirstName': 'Mariam', 'LastName': 'Aly', 'PI_MID_INIT': None, 'PI_SUFX_NAME': None, 'PI_FULL_NAME': 'Mariam Aly', 'EmailAddress': 'mariamaly@berkeley.edu', 'NSF_ID': '000772064', 'StartDate': '07/09/2024', 'EndDate': None, 'RoleCode': 'Principal Investigator'}

{'Name': 'University of California-Berkeley', 'CityName': 'BERKELEY', 'ZipCode': '947101749', 'PhoneNumber': '5106433891', 'StreetAddress': '1608 4TH ST STE 201', 'StreetAddress2': None, 'CountryName': 'United States', 'StateName': 'California', 'StateCode': 'CA', 'CONGRESSDISTRICT': '12', 'CONGRESS_DISTRICT_ORG': 'CA12', 'ORG_UEI_NUM': 'GS3YEVSS12N6', 'ORG_LGL_BUS_NAME': 'REGENTS OF THE UNIVERSITY OF CALIFORNIA, THE', 'ORG_PRNT_UEI_NUM': None}

{'Name': 'University of California-Berkeley', 'CityName': 'BERKELEY', 'StateCode': 'CA', 'ZipCode': '947101749', 'StreetAddress': '1608 4TH ST STE 201', 'CountryCode': 'US', 'CountryName': 'United States', 'StateName': 'California', 'CountryFlag': '1', 'CONGRESSDISTRICT': '12', 'CONGRESS_DISTRICT_PERF': 'CA12'}

{'Code': '199700', 'Text': 'NSF Research Traineeship (NRT)'}

2022~79592

{'url': 'https://www.nsf.gov/awardsearch/download?DownloadFileName=2024&All=true', 'xml': '2436430.xml'}

Conference: NeTS Early-Career Investigators Workshop 2024

NSF

07/15/2024

06/30/2025

{'Value': 'Standard Grant'}

{'Code': '05050000', 'Directorate': {'Abbreviation': 'CSE', 'LongName': 'Direct For Computer & Info Scie & Enginr'}, 'Division': {'Abbreviation': 'CNS', 'LongName': 'Division Of Computer and Network Systems'}}

{'SignBlockName': 'Darleen Fisher', 'PO_EMAI': 'dlfisher@nsf.gov', 'PO_PHON': '7032928950'}

The Networking Technology and Systems Early-Career Investigators (NeTS-ECI) Workshop will provide NeTS researchers an opportunity to understand, engage with, and address the challenges associated with developing and executing a research agenda as a new Principal Investigator (PI) in the domain of computer and information networking. NeTS-ECI allows early-career researchers to interact with their peers as well as leaders in the space, in interactive discussions and hands-on breakout activities aimed at developing all aspects of their career from developing research agendas to advising students to long-horizon research success. Each participant will have the opportunity to provide research materials and a short research pitch which will be presented at the workshop and discussed with their peers and mentors with expertise in the space. We expect the workshop will facilitate collaborative network, collaborations, and professional development. The output of the workshop will be a report summarizing the presentations, breakouts, and discussions. The workshop has historically, and will continue, to play an important role in developing early-career researchers in computer and information networking.<br/><br/>This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

07/24/2024

07/24/2024

None

Grant

47.070

1

4900

4900

2436457

[{'FirstName': 'Paul', 'LastName': 'Pearce', 'PI_MID_INIT': None, 'PI_SUFX_NAME': None, 'PI_FULL_NAME': 'Paul Pearce', 'EmailAddress': 'pearce@gatech.edu', 'NSF_ID': '000785074', 'StartDate': '07/24/2024', 'EndDate': None, 'RoleCode': 'Principal Investigator'}, {'FirstName': 'Christopher', 'LastName': 'Brinton', 'PI_MID_INIT': 'G', 'PI_SUFX_NAME': None, 'PI_FULL_NAME': 'Christopher G Brinton', 'EmailAddress': 'cgb@purdue.edu', 'NSF_ID': '000802115', 'StartDate': '07/24/2024', 'EndDate': None, 'RoleCode': 'Co-Principal Investigator'}, {'FirstName': 'Justine', 'LastName': 'Sherry', 'PI_MID_INIT': None, 'PI_SUFX_NAME': None, 'PI_FULL_NAME': 'Justine Sherry', 'EmailAddress': 'justines@andrew.cmu.edu', 'NSF_ID': '000728919', 'StartDate': '07/24/2024', 'EndDate': None, 'RoleCode': 'Co-Principal Investigator'}]

{'Name': 'Georgia Tech Research Corporation', 'CityName': 'ATLANTA', 'ZipCode': '303186395', 'PhoneNumber': '4048944819', 'StreetAddress': '926 DALNEY ST NW', 'StreetAddress2': None, 'CountryName': 'United States', 'StateName': 'Georgia', 'StateCode': 'GA', 'CONGRESSDISTRICT': '05', 'CONGRESS_DISTRICT_ORG': 'GA05', 'ORG_UEI_NUM': 'EMW9FC8J3HN4', 'ORG_LGL_BUS_NAME': 'GEORGIA TECH RESEARCH CORP', 'ORG_PRNT_UEI_NUM': 'EMW9FC8J3HN4'}

{'Name': 'Georgia Institute of Technology', 'CityName': 'ATLANTA', 'StateCode': 'GA', 'ZipCode': '303320001', 'StreetAddress': '225 North Avenue', 'CountryCode': 'US', 'CountryName': 'United States', 'StateName': 'Georgia', 'CountryFlag': '1', 'CONGRESSDISTRICT': '05', 'CONGRESS_DISTRICT_PERF': 'GA05'}

{'Code': '736300', 'Text': 'Networking Technology and Syst'}

2024~87421

{'url': 'https://www.nsf.gov/awardsearch/download?DownloadFileName=2024&All=true', 'xml': '2436457.xml'}

Conference: Navigating the Benefits and Risks of Publishing Studies of In Silico Modeling and Computational Approaches of Biological Agents and Organisms: A Workshop

NSF

08/01/2024

07/31/2025

{'Value': 'Standard Grant'}

{'Code': '08070000', 'Directorate': {'Abbreviation': 'BIO', 'LongName': 'Direct For Biological Sciences'}, 'Division': {'Abbreviation': 'MCB', 'LongName': 'Div Of Molecular and Cellular Bioscience'}}

{'SignBlockName': 'Bianca Garner', 'PO_EMAI': 'bgarner@nsf.gov', 'PO_PHON': '7032927587'}

In response to a request by the National Science Foundation, the Board on Life Sciences of the Division on Earth and Life Studies of the National Academies of Sciences, Engineering, and Medicine (National Academies), will plan and facilitate a workshop to define critical issues associated with the publication of studies involving the use of in silico modeling and computational approaches to understand and design biological systems that may fall under the purview of the May 2024 U.S. Government Policy for Oversight of Dual Use Research of Concern and Pathogens with Enhanced Pandemic Potential. For many decades, the coupling of biological sciences with other scientific disciplines has transformed scientific pursuits in biology leading to the development of new technologies, knowledge, and materials for a variety of fields. However, some have expressed concern that these advances are happening at a pace that increases risk across a variety of safety, security, and ethical areas. In 2004, following a meeting of the National Academy of Sciences on scientific communication and national security, a group of journal editors and authors issued a joint statement on considerations of biodefense and biosecurity, laying the groundwork for principles by which journals could proactively maximize public benefit while minimizing the risks of malicious use of biological research. During the past two decades, situations involving security concerns about the publication or communication of certain biological data have highlighted challenges in the uncertainty in assessing risks, lack of accessible resources in analyzing benefits and risks together, and lack of training of peer reviewers of manuscripts submitted to journals seeking input on dual use potential of the information provided. Within this context, the anticipated outcomes of the proposed project are a better understanding of how to assess the benefits and risks, and mitigate the risks to safeguard the benefits of research at the computational/biological intersection at the publication stage; identification of governance options for scientific journals, researchers, institutional administrators, and funders to communicate advances in in silico biology to effectively manage the risks in a manner that continue scientific progress and benefits; and deeper consideration of the scope of and approaches for implementing Section 6.2.2. in the May 2024 U.S. Government Policy for Oversight of Dual Use Research of Concern and Pathogens with Enhanced Pandemic Potential. This project builds on two decades of work by science journals, the National Academies, the U.S. government, intergovernmental organizations, and various nongovernmental and academic organizations.<br/><br/>The National Academies will conduct a workshop on opportunities for considering and navigating benefits and biosecurity risks of communicating studies involving computational modeling and analysis, including the use of generative artificial intelligence, of biological systems at the publication stage. The workshop will cover: a) existing statements, policies and guidance, and risk mitigation practices (or safeguards) on dual use research of concern and pathogens of pandemic potential relevant to communication and publication of covered scientific activities; b) challenges and needs to effectively safeguard the benefits and promote the advances of use of computational models and analysis with biological systems, while also mitigating the risks of generating or releasing these models and associated biological information that may be presented to human, animal, plant, and/or ecological health and/or national security; c) relevance and utility of evolving efforts on AI safety to communication of studies involving in silico modeling and analysis and/or generative AI with biological systems; and d) policy options and norms for safeguarding the benefits and advances while reducing the risks. Experts from leading scientific journals that publish research in biology and associated fields, professional societies and associations, Academies of Science, academia, industry, and other nongovernmental entities will be invited to the workshop. Critical themes from the workshop discussion will be summarized in a full proceedings, which will be posted on the National Academies Press website.<br/><br/>This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

07/25/2024

07/25/2024

None

Grant

47.074

1

4900

4900

2436547

[{'FirstName': 'Audrey', 'LastName': 'Thevenon', 'PI_MID_INIT': 'F', 'PI_SUFX_NAME': None, 'PI_FULL_NAME': 'Audrey F Thevenon', 'EmailAddress': 'athevenon@nas.edu', 'NSF_ID': '000767832', 'StartDate': '07/25/2024', 'EndDate': None, 'RoleCode': 'Co-Principal Investigator'}, {'FirstName': 'Kavita', 'LastName': 'Berger', 'PI_MID_INIT': 'M', 'PI_SUFX_NAME': None, 'PI_FULL_NAME': 'Kavita M Berger', 'EmailAddress': 'kberger@nas.edu', 'NSF_ID': '000809722', 'StartDate': '07/25/2024', 'EndDate': None, 'RoleCode': 'Principal Investigator'}]

{'Name': 'National Academy of Sciences', 'CityName': 'WASHINGTON', 'ZipCode': '204180007', 'PhoneNumber': '2023342254', 'StreetAddress': '2101 CONSTITUTION AVE NW', 'StreetAddress2': None, 'CountryName': 'United States', 'StateName': 'District of Columbia', 'StateCode': 'DC', 'CONGRESSDISTRICT': '00', 'CONGRESS_DISTRICT_ORG': 'DC00', 'ORG_UEI_NUM': 'PKFJZHG2MLG9', 'ORG_LGL_BUS_NAME': 'NATIONAL ACADEMY OF SCIENCES', 'ORG_PRNT_UEI_NUM': 'PKFJZHG2MLG9'}

{'Name': 'National Academies of Sciences, Engineering and Medicine', 'CityName': 'WASHINGTON', 'StateCode': 'DC', 'ZipCode': '200012703', 'StreetAddress': '500 Fifth Street NW', 'CountryCode': 'US', 'CountryName': 'United States', 'StateName': 'District of Columbia', 'CountryFlag': '1', 'CONGRESSDISTRICT': '00', 'CONGRESS_DISTRICT_PERF': 'DC00'}

{'Code': '727500', 'Text': 'Cross-BIO Activities'}

2024~399967

{'url': 'https://www.nsf.gov/awardsearch/download?DownloadFileName=2024&All=true', 'xml': '2436547.xml'}

Collaborative Research: FDT-BioTech: Aspects of Digital Twin Studies for Neuroimages

NSF

09/01/2024

08/31/2027

{'Value': 'Standard Grant'}

{'Code': '03040000', 'Directorate': {'Abbreviation': 'MPS', 'LongName': 'Direct For Mathematical & Physical Scien'}, 'Division': {'Abbreviation': 'DMS', 'LongName': 'Division Of Mathematical Sciences'}}

{'SignBlockName': 'Zhilan Feng', 'PO_EMAI': 'zfeng@nsf.gov', 'PO_PHON': '7032927523'}

Neurodegenerative diseases (for example, Alzheimer's disease, Parkinson's disease, multiple sclerosis) impact millions of people in the United States and result in hundreds of thousands of deaths. These disorders can affect people of all ages, although they are more common in older adults. Digital twin models, leveraging the exponential growth of biomedical data and artificial intelligence and data science techniques, are opening exciting avenues to obtain new insights into these diseases and revolutionize their treatment and prevention. The investigators will address multiple problems on this interface, and develop data science-driven theoretical foundations, methodological tools and algorithmic principles for several aspects of digital twin models towards better understanding of digital twins as a whole, and in particular in the context of their use in neuroscience and in prevention, treatment and better understanding of neurodegenerative diseases. They will also address the ethical, legal, and social implications of using digital twin models in the context of healthcare in general, and in studying neurodegenerative diseases using magnetic resonance-technology driven images (MRI) in particular. This research will greatly aid in the deployment of digital twins in medical and healthcare practice, and will significantly advance neuroscience and the study of neurodegenerative diseases.<br/><br/>The investigators will address open problems in low-dimensional manifold learning, causal pathway searches and feature discoveries and selections, and develop multiple techniques for verification, validation and uncertainty quantification of digital twins using Bayesian techniques, data assimilation, resampling, empirical likelihood methods and topological data analysis. They will also develop dynamical system models, incorporating observational image data, for computational efficiency and synthetic data generation for ethical use of artificial intelligence and digital twin technology in studying neurodegenerative diseases. Additionally, they will develop knowledge graph driven systems for use by regulatory and other healthcare monitoring agencies for de-risking and easy implementation of data-driven modern technologies. The investigators will work in conjunction with regulatory and other healthcare governing agencies towards better understanding of neurodegenerative diseases and successful deployment of data-driven technologies to mitigate suffering from such diseases. The investigators will mentor, train and teach students on various aspects of digital twins, data science and neuroscience and their interconnections, and will help build a highly skilled workforce on these topics.<br/><br/>This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

08/13/2024

08/13/2024

None

Grant

47.049

1

4900

4900

2436549

[{'FirstName': 'Snigdhansu', 'LastName': 'Chatterjee', 'PI_MID_INIT': 'B', 'PI_SUFX_NAME': None, 'PI_FULL_NAME': 'Snigdhansu B Chatterjee', 'EmailAddress': 'snigchat@umbc.edu', 'NSF_ID': '000404934', 'StartDate': '08/13/2024', 'EndDate': None, 'RoleCode': 'Principal Investigator'}, {'FirstName': 'Karuna', 'LastName': 'Joshi', 'PI_MID_INIT': None, 'PI_SUFX_NAME': None, 'PI_FULL_NAME': 'Karuna Joshi', 'EmailAddress': 'kjoshi1@umbc.edu', 'NSF_ID': '000643548', 'StartDate': '08/13/2024', 'EndDate': None, 'RoleCode': 'Co-Principal Investigator'}, {'FirstName': 'Animikh', 'LastName': 'Biswas', 'PI_MID_INIT': None, 'PI_SUFX_NAME': None, 'PI_FULL_NAME': 'Animikh Biswas', 'EmailAddress': 'abiswas@umbc.edu', 'NSF_ID': '000292608', 'StartDate': '08/13/2024', 'EndDate': None, 'RoleCode': 'Co-Principal Investigator'}]

{'Name': 'University of Maryland Baltimore County', 'CityName': 'BALTIMORE', 'ZipCode': '212500001', 'PhoneNumber': '4104553140', 'StreetAddress': '1000 HILLTOP CIR', 'StreetAddress2': None, 'CountryName': 'United States', 'StateName': 'Maryland', 'StateCode': 'MD', 'CONGRESSDISTRICT': '07', 'CONGRESS_DISTRICT_ORG': 'MD07', 'ORG_UEI_NUM': 'RNKYWXURFRL5', 'ORG_LGL_BUS_NAME': 'UNIVERSITY OF MARYLAND BALTIMORE COUNTY', 'ORG_PRNT_UEI_NUM': None}

{'Name': 'University of Maryland Baltimore County', 'CityName': 'BALTIMORE', 'StateCode': 'MD', 'ZipCode': '212500001', 'StreetAddress': '1000 HILLTOP CIR', 'CountryCode': 'US', 'CountryName': 'United States', 'StateName': 'Maryland', 'CountryFlag': '1', 'CONGRESSDISTRICT': '07', 'CONGRESS_DISTRICT_PERF': 'MD07'}

[{'Code': '125300', 'Text': 'OFFICE OF MULTIDISCIPLINARY AC'}, {'Code': '126900', 'Text': 'STATISTICS'}, {'Code': '745400', 'Text': 'MSPA-INTERDISCIPLINARY'}, {'Code': 'Y18200', 'Text': None}]

2024~888680

{'url': 'https://www.nsf.gov/awardsearch/download?DownloadFileName=2024&All=true', 'xml': '2436549.xml'}

Collaborative Research: FDT-BioTech: Aspects of Digital Twin Studies for Neuroimages

NSF

09/01/2024

08/31/2027

{'Value': 'Standard Grant'}

{'Code': '03040000', 'Directorate': {'Abbreviation': 'MPS', 'LongName': 'Direct For Mathematical & Physical Scien'}, 'Division': {'Abbreviation': 'DMS', 'LongName': 'Division Of Mathematical Sciences'}}

{'SignBlockName': 'Zhilan Feng', 'PO_EMAI': 'zfeng@nsf.gov', 'PO_PHON': '7032927523'}

Neurodegenerative diseases (for example, Alzheimer's disease, Parkinson's disease, multiple sclerosis) impact millions of people in the United States and result in hundreds of thousands of deaths. These disorders can affect people of all ages, although they are more common in older adults. Digital twin models, leveraging the exponential growth of biomedical data and artificial intelligence and data science techniques, are opening exciting avenues to obtain new insights into these diseases and revolutionize their treatment and prevention. The investigators will address multiple problems on this interface, and develop data science-driven theoretical foundations, methodological tools and algorithmic principles for several aspects of digital twin models towards better understanding of digital twins as a whole, and in particular in the context of their use in neuroscience and in prevention, treatment and better understanding of neurodegenerative diseases. They will also address the ethical, legal, and social implications of using digital twin models in the context of healthcare in general, and in studying neurodegenerative diseases using magnetic resonance-technology driven images (MRI) in particular. This research will greatly aid in the deployment of digital twins in medical and healthcare practice, and will significantly advance neuroscience and the study of neurodegenerative diseases.<br/><br/>The investigators will address open problems in low-dimensional manifold learning, causal pathway searches and feature discoveries and selections, and develop multiple techniques for verification, validation and uncertainty quantification of digital twins using Bayesian techniques, data assimilation, resampling, empirical likelihood methods and topological data analysis. They will also develop dynamical system models, incorporating observational image data, for computational efficiency and synthetic data generation for ethical use of artificial intelligence and digital twin technology in studying neurodegenerative diseases. Additionally, they will develop knowledge graph driven systems for use by regulatory and other healthcare monitoring agencies for de-risking and easy implementation of data-driven modern technologies. The investigators will work in conjunction with regulatory and other healthcare governing agencies towards better understanding of neurodegenerative diseases and successful deployment of data-driven technologies to mitigate suffering from such diseases. The investigators will mentor, train and teach students on various aspects of digital twins, data science and neuroscience and their interconnections, and will help build a highly skilled workforce on these topics.<br/><br/>This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

08/13/2024

08/13/2024

None

Grant

47.049

1

4900

4900

2436550

{'FirstName': 'Christophe', 'LastName': 'Lenglet', 'PI_MID_INIT': 'P', 'PI_SUFX_NAME': None, 'PI_FULL_NAME': 'Christophe P Lenglet', 'EmailAddress': 'clenglet@umn.edu', 'NSF_ID': '000575880', 'StartDate': '08/13/2024', 'EndDate': None, 'RoleCode': 'Principal Investigator'}

{'Name': 'University of Minnesota-Twin Cities', 'CityName': 'MINNEAPOLIS', 'ZipCode': '554552009', 'PhoneNumber': '6126245599', 'StreetAddress': '200 OAK ST SE', 'StreetAddress2': None, 'CountryName': 'United States', 'StateName': 'Minnesota', 'StateCode': 'MN', 'CONGRESSDISTRICT': '05', 'CONGRESS_DISTRICT_ORG': 'MN05', 'ORG_UEI_NUM': 'KABJZBBJ4B54', 'ORG_LGL_BUS_NAME': 'REGENTS OF THE UNIVERSITY OF MINNESOTA', 'ORG_PRNT_UEI_NUM': None}

{'Name': 'University of Minnesota-Twin Cities', 'CityName': 'MINNEAPOLIS', 'StateCode': 'MN', 'ZipCode': '554552009', 'StreetAddress': '200 OAK ST SE', 'CountryCode': 'US', 'CountryName': 'United States', 'StateName': 'Minnesota', 'CountryFlag': '1', 'CONGRESSDISTRICT': '05', 'CONGRESS_DISTRICT_PERF': 'MN05'}

{'Code': '745400', 'Text': 'MSPA-INTERDISCIPLINARY'}

2024~38280

{'url': 'https://www.nsf.gov/awardsearch/download?DownloadFileName=2024&All=true', 'xml': '2436550.xml'}

Collaborative Research: FDT-BioTech: Aspects of Digital Twin Studies for Neuroimages

NSF

09/01/2024

08/31/2027

{'Value': 'Standard Grant'}

{'Code': '03040000', 'Directorate': {'Abbreviation': 'MPS', 'LongName': 'Direct For Mathematical & Physical Scien'}, 'Division': {'Abbreviation': 'DMS', 'LongName': 'Division Of Mathematical Sciences'}}

{'SignBlockName': 'Zhilan Feng', 'PO_EMAI': 'zfeng@nsf.gov', 'PO_PHON': '7032927523'}

Neurodegenerative diseases (for example, Alzheimer's disease, Parkinson's disease, multiple sclerosis) impact millions of people in the United States and result in hundreds of thousands of deaths. These disorders can affect people of all ages, although they are more common in older adults. Digital twin models, leveraging the exponential growth of biomedical data and artificial intelligence and data science techniques, are opening exciting avenues to obtain new insights into these diseases and revolutionize their treatment and prevention. The investigators will address multiple problems on this interface, and develop data science-driven theoretical foundations, methodological tools and algorithmic principles for several aspects of digital twin models towards better understanding of digital twins as a whole, and in particular in the context of their use in neuroscience and in prevention, treatment and better understanding of neurodegenerative diseases. They will also address the ethical, legal, and social implications of using digital twin models in the context of healthcare in general, and in studying neurodegenerative diseases using magnetic resonance-technology driven images (MRI) in particular. This research will greatly aid in the deployment of digital twins in medical and healthcare practice, and will significantly advance neuroscience and the study of neurodegenerative diseases.<br/><br/>The investigators will address open problems in low-dimensional manifold learning, causal pathway searches and feature discoveries and selections, and develop multiple techniques for verification, validation and uncertainty quantification of digital twins using Bayesian techniques, data assimilation, resampling, empirical likelihood methods and topological data analysis. They will also develop dynamical system models, incorporating observational image data, for computational efficiency and synthetic data generation for ethical use of artificial intelligence and digital twin technology in studying neurodegenerative diseases. Additionally, they will develop knowledge graph driven systems for use by regulatory and other healthcare monitoring agencies for de-risking and easy implementation of data-driven modern technologies. The investigators will work in conjunction with regulatory and other healthcare governing agencies towards better understanding of neurodegenerative diseases and successful deployment of data-driven technologies to mitigate suffering from such diseases. The investigators will mentor, train and teach students on various aspects of digital twins, data science and neuroscience and their interconnections, and will help build a highly skilled workforce on these topics.<br/><br/>This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

08/13/2024

08/13/2024

None

Grant

47.049

1

4900

4900

2436551

{'FirstName': 'ASIM', 'LastName': 'DEY', 'PI_MID_INIT': 'K', 'PI_SUFX_NAME': None, 'PI_FULL_NAME': 'ASIM K DEY', 'EmailAddress': 'adey@utep.edu', 'NSF_ID': '000835487', 'StartDate': '08/13/2024', 'EndDate': None, 'RoleCode': 'Principal Investigator'}

{'Name': 'University of Texas at El Paso', 'CityName': 'EL PASO', 'ZipCode': '799688900', 'PhoneNumber': '9157475680', 'StreetAddress': '500 W UNIVERSITY AVE', 'StreetAddress2': None, 'CountryName': 'United States', 'StateName': 'Texas', 'StateCode': 'TX', 'CONGRESSDISTRICT': '16', 'CONGRESS_DISTRICT_ORG': 'TX16', 'ORG_UEI_NUM': 'C1DEGMMKC7W7', 'ORG_LGL_BUS_NAME': 'THE UNIVERSITY OF TEXAS AT EL PASO', 'ORG_PRNT_UEI_NUM': 'C1DEGMMKC7W7'}

{'Name': 'University of Texas at El Paso', 'CityName': 'EL PASO', 'StateCode': 'TX', 'ZipCode': '799680001', 'StreetAddress': '500 W UNIVERSITY AVE', 'CountryCode': 'US', 'CountryName': 'United States', 'StateName': 'Texas', 'CountryFlag': '1', 'CONGRESSDISTRICT': '16', 'CONGRESS_DISTRICT_PERF': 'TX16'}

{'Code': '745400', 'Text': 'MSPA-INTERDISCIPLINARY'}

2024~26965

{'url': 'https://www.nsf.gov/awardsearch/download?DownloadFileName=2024&All=true', 'xml': '2436551.xml'}

Controlling Electron, Magnon, and Phonon States in Quasi-2D Antiferromagnetic Semiconductors for Enabling Novel Device Functionalities

NSF

07/01/2024

02/28/2026

{'Value': 'Continuing Grant'}

{'Code': '03070000', 'Directorate': {'Abbreviation': 'MPS', 'LongName': 'Direct For Mathematical & Physical Scien'}, 'Division': {'Abbreviation': 'DMR', 'LongName': 'Division Of Materials Research'}}

{'SignBlockName': 'Paul Lane', 'PO_EMAI': 'plane@nsf.gov', 'PO_PHON': '7032922453'}

Non-technical Description<br/><br/>This research addresses the properties of a new class of ultra-thin quasi-two-dimensional semiconductors with intrinsic magnetic properties. The exotic properties of this new class of semiconductors make them particularly interesting for fundamental science research and practical applications. The investigators will explore the electronic, magnetic, and thermal properties of these unique materials with thicknesses of a few-atomic layers only. The PIs will also investigate the potential of these materials for use in devices with novel functionality that operate at high speed with low-energy dissipation. This research aligns with the Nation’s need for the development and research of novel semiconductor materials and devices under the recent CHIPS and Science Act. The interdisciplinary nature of the project will facilitate the involvement of students in the proposed research and contribute to undergraduate and graduate STEM education. The project team has developed a detailed Broadening Participation Plan that will impact the K-12, undergraduate, and graduate education of minorities underrepresented in STEM fields.<br/><br/>Technical Description<br/><br/>Transition-metal phospho-trichalcogenides span a wide variety of compounds with different electronic, magnetic, and phonon properties. These materials are one of a few van der Waals layered structures which can have intrinsic antiferromagnetism, even at mono-layer thickness. The band gap of these materials varies from ~1.3 eV to ~3.5 eV based on the type of its transition- metal element. Theory suggests that the application of gate bias and strain can induce phase transitions in these materials, changing their properties. While electrical insulators and conductors with AFM spin order have been studied extensively, little is known experimentally about antiferromagnetic layered semiconductors. This project aims to investigate the electron, phonon, and magnon properties of these unique materials at single- and few-layer structures, and to assess the possibilities of controlling their properties for enabling novel device functionalities. To achieve these goals, various types of these compounds will be synthesized and characterized using cryogenic micro – Brillouin – Raman spectroscopy, and electrical and thermal transport measurements. The results of this interdisciplinary research will add to the core knowledge in several areas of material science and electrical engineering, thereby delivering a transformative impact for applications of antiferromagnetic layered semiconductors. The intellectual merit of this project include knowledge of phonon and magnon band structures, and their modification with the thickness, strain, and electric bias; experimental data for controlling the Néel temperature in two- dimensional antiferromagnetic semiconductor films; mechanisms and methods for tuning the phase transitions in AFM semiconductors under the action of gate and strain; innovative approaches for enabling novel device functionalities via control of the electron, phonon, and magnon states in two-dimensional antiferromagnetic semiconductors.<br/><br/>This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

06/27/2024

08/27/2024

None

Grant

47.049

1

4900

4900

2436557

{'FirstName': 'Fariborz', 'LastName': 'Kargar', 'PI_MID_INIT': None, 'PI_SUFX_NAME': None, 'PI_FULL_NAME': 'Fariborz Kargar', 'EmailAddress': 'fkargar@auburn.edu', 'NSF_ID': '000813284', 'StartDate': '06/27/2024', 'EndDate': None, 'RoleCode': 'Principal Investigator'}

{'Name': 'Auburn University', 'CityName': 'AUBURN', 'ZipCode': '368490001', 'PhoneNumber': '3348444438', 'StreetAddress': '321-A INGRAM HALL', 'StreetAddress2': None, 'CountryName': 'United States', 'StateName': 'Alabama', 'StateCode': 'AL', 'CONGRESSDISTRICT': '03', 'CONGRESS_DISTRICT_ORG': 'AL03', 'ORG_UEI_NUM': 'DMQNDJDHTDG4', 'ORG_LGL_BUS_NAME': 'AUBURN UNIVERSITY', 'ORG_PRNT_UEI_NUM': 'DMQNDJDHTDG4'}

{'Name': 'Auburn University', 'CityName': 'AUBURN', 'StateCode': 'AL', 'ZipCode': '368490001', 'StreetAddress': '321-A INGRAM HALL', 'CountryCode': 'US', 'CountryName': 'United States', 'StateName': 'Alabama', 'CountryFlag': '1', 'CONGRESSDISTRICT': '03', 'CONGRESS_DISTRICT_PERF': 'AL03'}

{'Code': '177500', 'Text': 'ELECTRONIC/PHOTONIC MATERIALS'}

2024~319745

{'url': 'https://www.nsf.gov/awardsearch/download?DownloadFileName=2024&All=true', 'xml': '2436557.xml'}

NSF Student Travel Grant for 2024 37th IEEE International System-on-Chip Conference (IEEE SoCC)

NSF

08/01/2024

07/31/2025

{'Value': 'Standard Grant'}

{'Code': '05010000', 'Directorate': {'Abbreviation': 'CSE', 'LongName': 'Direct For Computer & Info Scie & Enginr'}, 'Division': {'Abbreviation': 'CCF', 'LongName': 'Division of Computing and Communication Foundations'}}

{'SignBlockName': 'Almadena Chtchelkanova', 'PO_EMAI': 'achtchel@nsf.gov', 'PO_PHON': '7032927498'}

The 37th Institute of Electrical and Electronics Engineering International System-on-Chip Conference (IEEE SoCC 2024) will be held September 16-19, 2024, in Dresden, Germany. For more than 36 years, the IEEE SoCC has been a premier forum for sharing the latest advancements in system-on-chip (SoC) architectures, systems, logic and circuit design, process technology, test, design tools, and application scenarios. The conference provides a unique platform for students to engage with leading experts, present their research, and network with professionals in the field. This project will support student travel to IEEE SoCC 2024 for up to 15 US-based participants. Supporting student participation in IEEE SoCC 2024 will significantly enhance the academic and professional development of young researchers. Exposure to cutting-edge research and networking opportunities with industry leaders and academic experts will enrich their educational experience. This exposure is crucial for inspiring innovative contributions to the field of SoC. Students will gain insights into the latest advancements, methodologies, and challenges in SoC design and implementation, which will inform and refine their research endeavors. Additionally, presenting their work at an esteemed conference will provide critical feedback and validation from the broader research community, further advancing their scholarly growth. <br/><br/>The travel grant will promote diversity and inclusion by enabling students from various backgrounds and institutions to participate in IEEE SoCC 2024. This initiative will help build a diverse pipeline of future leaders in the SoC community, ensuring that different perspectives and ideas contribute to the advancement of the field. By supporting students who might otherwise face financial barriers, the grant will foster an inclusive environment where talent and potential are the primary criteria for participation. This inclusivity is essential for driving innovation and addressing the complex challenges in SoC technology. The broader impacts of this initiative extend to enhancing the overall quality and diversity of the research community, promoting equitable opportunities for all students, and ultimately contributing to the development of robust and versatile SoC solutions that can benefit society at large.<br/><br/>This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

07/26/2024

07/26/2024

None

Grant

47.070

1

4900

4900

2436586

{'FirstName': 'Magdy', 'LastName': 'Bayoumi', 'PI_MID_INIT': 'A', 'PI_SUFX_NAME': None, 'PI_FULL_NAME': 'Magdy A Bayoumi', 'EmailAddress': 'magdy.bayoumi@louisiana.edu', 'NSF_ID': '000849589', 'StartDate': '07/26/2024', 'EndDate': None, 'RoleCode': 'Principal Investigator'}

{'Name': 'University of Louisiana at Lafayette', 'CityName': 'LAFAYETTE', 'ZipCode': '705032014', 'PhoneNumber': '3374825811', 'StreetAddress': '104 E UNIVERSITY AVE', 'StreetAddress2': None, 'CountryName': 'United States', 'StateName': 'Louisiana', 'StateCode': 'LA', 'CONGRESSDISTRICT': '03', 'CONGRESS_DISTRICT_ORG': 'LA03', 'ORG_UEI_NUM': 'C169K7T4QZ96', 'ORG_LGL_BUS_NAME': 'UNIVERSITY OF LOUISIANA AT LAFAYETTE', 'ORG_PRNT_UEI_NUM': 'C169K7T4QZ96'}

{'Name': 'University of Louisiana at Lafayette', 'CityName': 'LAFAYETTE', 'StateCode': 'LA', 'ZipCode': '705032014', 'StreetAddress': '104 E UNIVERSITY AVE', 'CountryCode': 'US', 'CountryName': 'United States', 'StateName': 'Louisiana', 'CountryFlag': '1', 'CONGRESSDISTRICT': '03', 'CONGRESS_DISTRICT_PERF': 'LA03'}

{'Code': '779800', 'Text': 'Software & Hardware Foundation'}

2024~10000

{'url': 'https://www.nsf.gov/awardsearch/download?DownloadFileName=2024&All=true', 'xml': '2436586.xml'}

The National Welding Hub for Advanced Welding Process Education and Training

NSF

10/01/2024

09/30/2027

{'Value': 'Standard Grant'}

{'Code': '11040100', 'Directorate': {'Abbreviation': 'EDU', 'LongName': 'Directorate for STEM Education'}, 'Division': {'Abbreviation': 'DUE', 'LongName': 'Division Of Undergraduate Education'}}

{'SignBlockName': 'Virginia Carter', 'PO_EMAI': 'vccarter@nsf.gov', 'PO_PHON': '7032924651'}

Welding and materials joining is an essential technology used across numerous industries. Throughout the US, the largest employers of welders and welding technicians include commercial building construction, infrastructure, agricultural equipment manufacturing, automotive manufacturing, oil and gas, shipbuilding, aerospace, energy, and metal fabrication. According to the Occupational Data Report developed by Lightcast in 2023, there's a projected need for 330,000 new welding professionals by 2028, or 82,500 annually between 2024-2028. A recent survey conducted by Weld-Ed found that enrollment has declined 8.7% in welding programs since the end of the pandemic. Yet the welding industry is rapidly evolving, driven by technological advancements from increased automation to new material usage. There has been a significant increase in the use of robotic welding systems that offer unparalleled precision, efficiency and consistency. The integration of artificial intelligence in these systems to allow for real-time adjustments and decision-making will only enhance their use. With the documented need for welders, the Weld-Ed Hub proposes to continue to support welding programs to ensure that industries will have the skilled technical welders needed.<br/><br/>The Weld-Ed Hub will provide fundamental welding technology, emerging welding technology, industry and education research data, best practice teaching methods to welding instructors and industry professionals. The Weld-Ed Hub project goal is to improve the number and quality of welding and materials joining technicians to meet industry workforce need. To attain this goal a series of objectives and activities will be supported, including: 1) Providing faculty professional development activities to improve the ability of welding instructors and welding programs to prepare welding technicians for the workforce; 2) Gathering and disseminating advanced material welding processes, emerging welding technology, and advanced inspection technology to welding instructors; 3) Recruiting new welding students and supporting the retention of welding students through career awareness, career guidance, and career assistance; and 4) Conducting ongoing research to determine the current and future state of welding and inspection technology and education and training delivery. This project is funded by the Advanced Technological Education program that focuses on the education of technicians for the advanced-technology fields that drive the nation's economy.<br/><br/>This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

08/12/2024

08/20/2024

None

Grant

47.076

1

4900

4900

2436592

[{'FirstName': 'Monica', 'LastName': 'Pfarr', 'PI_MID_INIT': None, 'PI_SUFX_NAME': None, 'PI_FULL_NAME': 'Monica Pfarr', 'EmailAddress': 'mpfarr@aws.org', 'NSF_ID': '000272936', 'StartDate': '08/12/2024', 'EndDate': None, 'RoleCode': 'Principal Investigator'}, {'FirstName': 'Michael', 'LastName': 'Fox', 'PI_MID_INIT': 'D', 'PI_SUFX_NAME': None, 'PI_FULL_NAME': 'Michael D Fox', 'EmailAddress': 'mfox@lorainccc.edu', 'NSF_ID': '000789521', 'StartDate': '08/12/2024', 'EndDate': None, 'RoleCode': 'Co-Principal Investigator'}, {'FirstName': 'W. Richard', 'LastName': 'Polanin', 'PI_MID_INIT': None, 'PI_SUFX_NAME': None, 'PI_FULL_NAME': 'W. Richard Polanin', 'EmailAddress': 'rpolanin@icc.edu', 'NSF_ID': '000571398', 'StartDate': '08/20/2024', 'EndDate': None, 'RoleCode': 'Co-Principal Investigator'}]

{'Name': 'Lorain County Community College', 'CityName': 'ELYRIA', 'ZipCode': '440351613', 'PhoneNumber': '4403655222', 'StreetAddress': '1005 N ABBE RD', 'StreetAddress2': None, 'CountryName': 'United States', 'StateName': 'Ohio', 'StateCode': 'OH', 'CONGRESSDISTRICT': '05', 'CONGRESS_DISTRICT_ORG': 'OH05', 'ORG_UEI_NUM': 'LF9NNSN2HGN3', 'ORG_LGL_BUS_NAME': 'LORAIN COUNTY COMMUNITY COLLEGE DISTRICT', 'ORG_PRNT_UEI_NUM': None}

{'Name': 'Lorain County Community College District', 'CityName': 'ELYRIA', 'StateCode': 'OH', 'ZipCode': '440351613', 'StreetAddress': '1005 N ABBE RD', 'CountryCode': 'US', 'CountryName': 'United States', 'StateName': 'Ohio', 'CountryFlag': '1', 'CONGRESSDISTRICT': '05', 'CONGRESS_DISTRICT_PERF': 'OH05'}

{'Code': '741200', 'Text': 'Advanced Tech Education Prog'}

2024~2425104

{'url': 'https://www.nsf.gov/awardsearch/download?DownloadFileName=2024&All=true', 'xml': '2436592.xml'}

CAREER: Surface Engineering by Predictive Laser Deposition of Multi-Principal Element Alloys

NSF

07/01/2024

02/28/2025

{'Value': 'Standard Grant'}

{'Code': '07030000', 'Directorate': {'Abbreviation': 'ENG', 'LongName': 'Directorate For Engineering'}, 'Division': {'Abbreviation': 'CMMI', 'LongName': 'Div Of Civil, Mechanical, & Manufact Inn'}}

{'SignBlockName': 'Pranav Soman', 'PO_EMAI': 'psoman@nsf.gov', 'PO_PHON': '7032924322'}

This Faculty Early Career Development Program (CAREER) award supports a transformative, experimentally-validated predictive framework to manufacture multi-principal element alloys (MPEAs). These alloys represent a new class of materials that, unlike conventional alloys such as steels, generally consist of five or more principal elements in significant proportions. Promising structural properties, such as superior mechanical strength and hardness, encourage their use as coatings for additively engineered surfaces. The processing of these materials presents some challenges, however, including uneven mixing and microstructure changes during rapid cooling that contribute to formation of cracks when these materials are deposited as coatings. This research project will build an understanding of the correlation of atomistic properties to system-scale processing parameters through the synergistic use of computational predictions, quantification of uncertainties in the processing conditions and material properties, and experimental characterization. The outcomes of the project will advance the manufacturability of these alloys and their surface coatings, which will have significant impact on many technological areas including propulsion, machinery, transportation, and medical devices. The predictive processing paradigm developed through this project will be widely applicable to an array of materials systems, and can bolster additive manufacturing processes with optimization capabilities. The tightly integrated educational and outreach activities are targeted to encourage students from underrepresented minorities to pursue opportunities in STEM fields, and simultaneously contribute towards gender equality and economic opportunities for impoverished communities. <br/><br/>The objective of this CAREER project is to generate new knowledge on how the diffusion of multiple principal elements in an alloy melt under rapid cooling affects the microstructure and properties of their laser deposited clads. To realize this objective, an integrated computational framework will be established that (1) marries together structure and property predictions from molecular dynamics simulations of the alloy melt with processing conditions, (2) provides recommendations for optimizing the manufacturing parameters for producing clads of desired composition and quality, and (3) imparts robustness to the correlations by electron microscopy and X-ray spectroscopy characterizations, and uncertainty quantification of the high-dimensional parameter space of compositions, impurities, and manufacturing environment variables. The direct and multiscale correlation of the simulation predictions to the system scale processing parameters, will facilitate the mapping of the parameters to targeted criteria space via a Pareto front. The interrelationship between the processing conditions (system scale) and the alloy melt dynamics (atomic scale) will enable intelligent parameter selection for the laser cladding and aid in creating surface coatings that are homogenous in composition and crack resistant.<br/><br/>This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

07/24/2024

07/24/2024

None

Grant

47.041

1

4900

4900

2436601

{'FirstName': 'Ganesh', 'LastName': 'Balasubramanian', 'PI_MID_INIT': None, 'PI_SUFX_NAME': None, 'PI_FULL_NAME': 'Ganesh Balasubramanian', 'EmailAddress': 'bganesh@newhaven.edu', 'NSF_ID': '000633694', 'StartDate': '07/24/2024', 'EndDate': None, 'RoleCode': 'Principal Investigator'}

{'Name': 'University of New Haven', 'CityName': 'WEST HAVEN', 'ZipCode': '065161916', 'PhoneNumber': '2039327000', 'StreetAddress': '300 BOSTON POST RD', 'StreetAddress2': None, 'CountryName': 'United States', 'StateName': 'Connecticut', 'StateCode': 'CT', 'CONGRESSDISTRICT': '03', 'CONGRESS_DISTRICT_ORG': 'CT03', 'ORG_UEI_NUM': 'FZBDVM1MBTN9', 'ORG_LGL_BUS_NAME': 'UNIVERSITY OF NEW HAVEN, INCORPORATED', 'ORG_PRNT_UEI_NUM': None}

{'Name': 'University of New Haven', 'CityName': 'WEST HAVEN', 'StateCode': 'CT', 'ZipCode': '065161916', 'StreetAddress': '300 BOSTON POST RD', 'CountryCode': 'US', 'CountryName': 'United States', 'StateName': 'Connecticut', 'CountryFlag': '1', 'CONGRESSDISTRICT': '03', 'CONGRESS_DISTRICT_PERF': 'CT03'}

[{'Code': '088Y00', 'Text': 'AM-Advanced Manufacturing'}, {'Code': '104500', 'Text': 'CAREER: FACULTY EARLY CAR DEV'}, {'Code': '150400', 'Text': 'GOALI-Grnt Opp Acad Lia wIndus'}]

['2020~158670', '2021~16000', '2022~16000', '2023~71000']

{'url': 'https://www.nsf.gov/awardsearch/download?DownloadFileName=2024&All=true', 'xml': '2436601.xml'}

Collaborative Research: FDT-BioTech:physics-informed and machine learning-accelerated digital twin simulations for cardiovascular medical device evaluation

NSF

12/01/2024

11/30/2027

{'Value': 'Standard Grant'}

{'Code': '03040000', 'Directorate': {'Abbreviation': 'MPS', 'LongName': 'Direct For Mathematical & Physical Scien'}, 'Division': {'Abbreviation': 'DMS', 'LongName': 'Division Of Mathematical Sciences'}}

{'SignBlockName': 'Yulia Gel', 'PO_EMAI': 'ygel@nsf.gov', 'PO_PHON': '7032927888'}

A digital twin is a virtual model that mirrors and updates in real-time based on data from its physical counterpart. In biomedical and healthcare fields, digital twins, representing virtual models of patients, medical devices, and more, can open up new avenues for developing and evaluating innovative biomedical technologies, particularly enabling virtual clinical trials for evaluating cardiovascular medical devices and advancing regulatory sciences. However, current digital twin technologies lack sufficient computational fidelity and efficiency to effectively support these biomedical and healthcare applications. To resolve these challenges, this project aims to develop advanced computational methods for creating high-fidelity, fast-running digital twins of patient hearts and cardiovascular medical devices. Additionally, the methods will be made publicly available through a software/cyberinfrastructure platform. This will facilitate virtual clinical trials that can evaluate the efficacy and safety of medical devices, as well as improve device designs before initiating real clinical trials in a safe, cost-effective, and precisely controlled manner. In addition to advancing digital twin technologies, the project’s cyberinfrastructure will serve as an educational resource for students, researchers, and industrial engineers to enhance their understanding of advanced digital twin techniques for medical device evaluation.<br/><br/>This project will develop novel machine learning (ML)-based image analysis algorithms and physics solvers for performing near-realtime virtual clinical trials with high-fidelity digital twins of patient hearts and cardiovascular medical devices. Patient-specific geometries and tissue mechanical properties will be incorporated into the digital twin construction for near-realtime physics simulations. Consequently, virtual clinical trials can be performed at significantly reduced time and financial costs. This project will deliver (1) novel ML algorithms for accurate digital twin geometry reconstruction from 3D+t medical images, enabling point-to-point mesh correspondence for high-fidelity dynamic motion tracking; (2) a robust and computationally efficient inverse method to identify in vivo material properties from medical images, which is essential for creating material-realistic digital twins; (3) a new ML-based fluid-structure interaction (ML-FSI) solver for biomechanics and hemodynamic analyses, thereby enabling dynamic digital twin simulations throughout a cardiac cycle. While the primary focus will be on digital twins of the left heart and aorta, the computational methods can be generally applied to create digital twins of the entire heart. The computational methods will be demonstrated through concrete examples involving Transcatheter Aortic Valve Replacement (TAVR) and Thoracic Endovascular Aortic Repair (TEVAR) devices. The algorithms and methods developed in this project will be generic and readily applicable to devices for treating various cardiovascular diseases.<br/><br/>This project is jointly funded by the Division of Mathematical Sciences, the OAC Cyberinfrastructure for Sustained Scientific Innovation (CSSI) program, and the CBET Engineering of Biomedical Systems program.<br/><br/>This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

08/13/2024

08/13/2024

None

Grant

47.041, 47.049, 47.070

1

4900

4900

2436629

{'FirstName': 'Liang', 'LastName': 'Liang', 'PI_MID_INIT': None, 'PI_SUFX_NAME': None, 'PI_FULL_NAME': 'Liang Liang', 'EmailAddress': 'liang@cs.miami.edu', 'NSF_ID': '000788814', 'StartDate': '08/13/2024', 'EndDate': None, 'RoleCode': 'Principal Investigator'}

{'Name': 'University of Miami', 'CityName': 'CORAL GABLES', 'ZipCode': '331462919', 'PhoneNumber': '3052843924', 'StreetAddress': '1320 SOUTH DIXIE HIGHWAY STE 650', 'StreetAddress2': None, 'CountryName': 'United States', 'StateName': 'Florida', 'StateCode': 'FL', 'CONGRESSDISTRICT': '27', 'CONGRESS_DISTRICT_ORG': 'FL27', 'ORG_UEI_NUM': 'RQMFJGDTQ5V3', 'ORG_LGL_BUS_NAME': 'UNIVERSITY OF MIAMI', 'ORG_PRNT_UEI_NUM': None}

{'Name': 'University of Miami', 'CityName': 'CORAL GABLES', 'StateCode': 'FL', 'ZipCode': '331462508', 'StreetAddress': '1365 Memorial Drive', 'CountryCode': 'US', 'CountryName': 'United States', 'StateName': 'Florida', 'CountryFlag': '1', 'CONGRESSDISTRICT': '27', 'CONGRESS_DISTRICT_PERF': 'FL27'}

[{'Code': '125300', 'Text': 'OFFICE OF MULTIDISCIPLINARY AC'}, {'Code': '534500', 'Text': 'Engineering of Biomed Systems'}, {'Code': '723100', 'Text': 'CYBERINFRASTRUCTURE'}, {'Code': '745400', 'Text': 'MSPA-INTERDISCIPLINARY'}]

2024~380161

{'url': 'https://www.nsf.gov/awardsearch/download?DownloadFileName=2024&All=true', 'xml': '2436629.xml'}

Collaborative Research: FDT-BioTech:physics-informed and machine learning-accelerated digital twin simulations for cardiovascular medical device evaluation

NSF

12/01/2024

11/30/2027

{'Value': 'Standard Grant'}

{'Code': '03040000', 'Directorate': {'Abbreviation': 'MPS', 'LongName': 'Direct For Mathematical & Physical Scien'}, 'Division': {'Abbreviation': 'DMS', 'LongName': 'Division Of Mathematical Sciences'}}

{'SignBlockName': 'Yulia Gel', 'PO_EMAI': 'ygel@nsf.gov', 'PO_PHON': '7032927888'}

A digital twin is a virtual model that mirrors and updates in real-time based on data from its physical counterpart. In biomedical and healthcare fields, digital twins, representing virtual models of patients, medical devices, and more, can open up new avenues for developing and evaluating innovative biomedical technologies, particularly enabling virtual clinical trials for evaluating cardiovascular medical devices and advancing regulatory sciences. However, current digital twin technologies lack sufficient computational fidelity and efficiency to effectively support these biomedical and healthcare applications. To resolve these challenges, this project aims to develop advanced computational methods for creating high-fidelity, fast-running digital twins of patient hearts and cardiovascular medical devices. Additionally, the methods will be made publicly available through a software/cyberinfrastructure platform. This will facilitate virtual clinical trials that can evaluate the efficacy and safety of medical devices, as well as improve device designs before initiating real clinical trials in a safe, cost-effective, and precisely controlled manner. In addition to advancing digital twin technologies, the project’s cyberinfrastructure will serve as an educational resource for students, researchers, and industrial engineers to enhance their understanding of advanced digital twin techniques for medical device evaluation.<br/><br/>This project will develop novel machine learning (ML)-based image analysis algorithms and physics solvers for performing near-realtime virtual clinical trials with high-fidelity digital twins of patient hearts and cardiovascular medical devices. Patient-specific geometries and tissue mechanical properties will be incorporated into the digital twin construction for near-realtime physics simulations. Consequently, virtual clinical trials can be performed at significantly reduced time and financial costs. This project will deliver (1) novel ML algorithms for accurate digital twin geometry reconstruction from 3D+t medical images, enabling point-to-point mesh correspondence for high-fidelity dynamic motion tracking; (2) a robust and computationally efficient inverse method to identify in vivo material properties from medical images, which is essential for creating material-realistic digital twins; (3) a new ML-based fluid-structure interaction (ML-FSI) solver for biomechanics and hemodynamic analyses, thereby enabling dynamic digital twin simulations throughout a cardiac cycle. While the primary focus will be on digital twins of the left heart and aorta, the computational methods can be generally applied to create digital twins of the entire heart. The computational methods will be demonstrated through concrete examples involving Transcatheter Aortic Valve Replacement (TAVR) and Thoracic Endovascular Aortic Repair (TEVAR) devices. The algorithms and methods developed in this project will be generic and readily applicable to devices for treating various cardiovascular diseases.<br/><br/>This project is jointly funded by the Division of Mathematical Sciences, the OAC Cyberinfrastructure for Sustained Scientific Innovation (CSSI) program, and the CBET Engineering of Biomedical Systems program.<br/><br/>This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

08/13/2024

08/13/2024

None

Grant

47.041, 47.049, 47.070

1

4900

4900

2436630

{'FirstName': 'Minliang', 'LastName': 'Liu', 'PI_MID_INIT': None, 'PI_SUFX_NAME': None, 'PI_FULL_NAME': 'Minliang Liu', 'EmailAddress': 'minliang.liu@ttu.edu', 'NSF_ID': '0000A0C85', 'StartDate': '08/13/2024', 'EndDate': None, 'RoleCode': 'Principal Investigator'}

{'Name': 'Texas Tech University', 'CityName': 'LUBBOCK', 'ZipCode': '79409', 'PhoneNumber': '8067423884', 'StreetAddress': '2500 BROADWAY', 'StreetAddress2': None, 'CountryName': 'United States', 'StateName': 'Texas', 'StateCode': 'TX', 'CONGRESSDISTRICT': '19', 'CONGRESS_DISTRICT_ORG': 'TX19', 'ORG_UEI_NUM': 'EGLKRQ5JBCZ7', 'ORG_LGL_BUS_NAME': 'TEXAS TECH UNIVERSITY SYSTEM', 'ORG_PRNT_UEI_NUM': None}

{'Name': 'Texas Tech University', 'CityName': 'LUBBOCK', 'StateCode': 'TX', 'ZipCode': '794091035', 'StreetAddress': '2500 BROADWAY', 'CountryCode': 'US', 'CountryName': 'United States', 'StateName': 'Texas', 'CountryFlag': '1', 'CONGRESSDISTRICT': '19', 'CONGRESS_DISTRICT_PERF': 'TX19'}

[{'Code': '125300', 'Text': 'OFFICE OF MULTIDISCIPLINARY AC'}, {'Code': '534500', 'Text': 'Engineering of Biomed Systems'}, {'Code': '723100', 'Text': 'CYBERINFRASTRUCTURE'}, {'Code': '745400', 'Text': 'MSPA-INTERDISCIPLINARY'}]

2024~367193

{'url': 'https://www.nsf.gov/awardsearch/download?DownloadFileName=2024&All=true', 'xml': '2436630.xml'}

Conference: International Indian Statistical Association 2024 Annual Flagship Conference in India

NSF

09/01/2024

08/31/2025

{'Value': 'Standard Grant'}

{'Code': '03040000', 'Directorate': {'Abbreviation': 'MPS', 'LongName': 'Direct For Mathematical & Physical Scien'}, 'Division': {'Abbreviation': 'DMS', 'LongName': 'Division Of Mathematical Sciences'}}

{'SignBlockName': 'Tapabrata Maiti', 'PO_EMAI': 'tmaiti@nsf.gov', 'PO_PHON': '7032925307'}

This project supports a five-day international conference at the Cochin University of Science and Technology (CUSAT) in Kochi, India, from December 27 to December 31, 2024. The conference serves as the official annual meeting of the International Indian Statistical Association (IISA). The conference provides its members a unique opportunity to meet and exchange ideas among researchers, and students with a broad focus on theoretical, methodological, and applied research across various scientific domains. Attendees can expect captivating plenary sessions, special invited talks, panel discussions, and a diverse array of invited and contributed sessions. IISA and CUSAT are the primary organizers of the conference. <br/><br/>The conference's main objective is to bring together well-established and emerging young researchers from around the world who are actively pursuing theoretical and methodological research in statistics, data science, and their applications in various allied fields. It aims to provide a forum for leading experts and young researchers to discuss recent progress in statistical theory and data science. The conference offers a vibrant agenda, including student paper competitions, insightful presentations, and awards, complemented by enriching workshops and the esteemed Early Career Award in Statistics and Data Science (ECASDS). It strives to maintain a healthy presence of women and minorities in all these categories and of young researchers (within five years of their doctoral degrees) in invited sessions. The meeting is primarily self-funded, with revenue primarily coming from registration. The revenue generated from the registration fee will be mainly used to cover the cost of the conference. The requested budget will cover registration, partial airfare, and lodging for 20 students and 8 junior researchers to support the participation of students studying at US institutions and US-based junior researchers in the 2024 IISA conference. The official website https://www.intindstat.org/conference2024/index provides details on different activities planned during the conference.<br/><br/>This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

08/13/2024

08/13/2024

None

Grant

47.049

1

4900

4900

2436649

{'FirstName': 'Saonli', 'LastName': 'Basu', 'PI_MID_INIT': None, 'PI_SUFX_NAME': None, 'PI_FULL_NAME': 'Saonli Basu', 'EmailAddress': 'saonli@umn.edu', 'NSF_ID': '000677796', 'StartDate': '08/13/2024', 'EndDate': None, 'RoleCode': 'Principal Investigator'}

{'Name': 'University of Minnesota-Twin Cities', 'CityName': 'MINNEAPOLIS', 'ZipCode': '554552009', 'PhoneNumber': '6126245599', 'StreetAddress': '200 OAK ST SE', 'StreetAddress2': None, 'CountryName': 'United States', 'StateName': 'Minnesota', 'StateCode': 'MN', 'CONGRESSDISTRICT': '05', 'CONGRESS_DISTRICT_ORG': 'MN05', 'ORG_UEI_NUM': 'KABJZBBJ4B54', 'ORG_LGL_BUS_NAME': 'REGENTS OF THE UNIVERSITY OF MINNESOTA', 'ORG_PRNT_UEI_NUM': None}

{'Name': 'University of Minnesota-Twin Cities', 'CityName': 'MINNEAPOLIS', 'StateCode': 'MN', 'ZipCode': '554552070', 'StreetAddress': '200 OAK ST SE', 'CountryCode': 'US', 'CountryName': 'United States', 'StateName': 'Minnesota', 'CountryFlag': '1', 'CONGRESSDISTRICT': '05', 'CONGRESS_DISTRICT_PERF': 'MN05'}

{'Code': '126900', 'Text': 'STATISTICS'}

2024~25000

{'url': 'https://www.nsf.gov/awardsearch/download?DownloadFileName=2024&All=true', 'xml': '2436649.xml'}

Participant Support for the 2025 Biomedical Engineering Society (BMES) – Cellular and Molecular Bioengineering (CMBE) Conference; Carlsbad, California; 3-6 January 2025

NSF

09/01/2024

08/31/2025

{'Value': 'Standard Grant'}

{'Code': '07030000', 'Directorate': {'Abbreviation': 'ENG', 'LongName': 'Directorate For Engineering'}, 'Division': {'Abbreviation': 'CMMI', 'LongName': 'Div Of Civil, Mechanical, & Manufact Inn'}}

{'SignBlockName': 'Shivani Sharma', 'PO_EMAI': 'shisharm@nsf.gov', 'PO_PHON': '7032924204'}

This grant provides travel reimbursements for students and postdoctoral fellows (12 Travel Awards and 10 Poster Awards) and young faculty (10 Rising Star Awards) to attend the 2025 Biomedical Engineering Society (BMES) – Cellular and Molecular Bioengineering (CMBE) Conference, which will be held 3-6 January 2025, in Carlsbad, California to broaden participation of junior researchers and individuals from underrepresented groups (including women and minorities). The specific objectives of this conference are: 1) to support graduate students, postdoctoral fellows, and independent junior investigators (Rising Stars) whose submitted abstracts are selected for podium presentations based on scientific merits, with a focus on promoting women and underrepresented minorities; 2) to converge mechanobiology and cell engineering within the CMBE research community; and 3) to promote interdisciplinary collaborations and career development and networking opportunities. The conference will integrate engineers and biologists with industrial and translational experience to encourage sharing research across disciplines toward the common goal of improving human health.<br/> <br/>The BMES-CMBE Conference has been held annually in the US and US territories since January 2013, organized by the CMBE Special Interest Group (SIG) under the BMES. The BMES-CMBE SIG is the only society and conference covering broad aspects of cellular and molecular bioengineering and the only group in this area backed by a major professional society. The theme of the 2025 conference will be “Cell Engineering for Mechanomedicine and Rejuvenation”. The program will focus on applying novel and advanced CMBE techniques, materials, and model systems to research in varied physiological and pathological systems and regenerative models. The conference will also feature advanced strategies and technologies to translate novel mechanobiology insights to improve human health. The choices of theme and invited speakers will bridge the gap between bench and bedside in CMBE technologies and specifically create synergistic interactions between cell biologists, engineers, and clinicians in both academia and industry.<br/><br/>This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

07/24/2024

07/24/2024

None

Grant

47.041

1

4900

4900

2436651

{'FirstName': 'Keyue', 'LastName': 'Shen', 'PI_MID_INIT': None, 'PI_SUFX_NAME': None, 'PI_FULL_NAME': 'Keyue Shen', 'EmailAddress': 'keyue.shen@usc.edu', 'NSF_ID': '000720113', 'StartDate': '07/24/2024', 'EndDate': None, 'RoleCode': 'Principal Investigator'}

{'Name': 'Biomedical Engineering Society', 'CityName': 'HYATTSVILLE', 'ZipCode': '207857226', 'PhoneNumber': '3014591999', 'StreetAddress': '8201 CORPORATE DR STE 1125', 'StreetAddress2': None, 'CountryName': 'United States', 'StateName': 'Maryland', 'StateCode': 'MD', 'CONGRESSDISTRICT': '04', 'CONGRESS_DISTRICT_ORG': 'MD04', 'ORG_UEI_NUM': 'SSMNEHNM5894', 'ORG_LGL_BUS_NAME': 'BIOMEDICAL ENGINEERING SOCIETY', 'ORG_PRNT_UEI_NUM': 'SSMNEHNM5894'}

{'Name': 'Cape Rey Carlsbad Beach Hilton', 'CityName': 'Carlsbad', 'StateCode': 'CA', 'ZipCode': '920114620', 'StreetAddress': '1 Ponto Rd', 'CountryCode': 'US', 'CountryName': 'United States', 'StateName': 'California', 'CountryFlag': '1', 'CONGRESSDISTRICT': '49', 'CONGRESS_DISTRICT_PERF': 'CA49'}

{'Code': '747900', 'Text': 'BMMB-Biomech & Mechanobiology'}

2024~20000

{'url': 'https://www.nsf.gov/awardsearch/download?DownloadFileName=2024&All=true', 'xml': '2436651.xml'}

Travel: NSF Student Travel Grant for 2024 ACM Conference on Computer and Communications Security (CCS)

NSF

10/01/2024

09/30/2025

{'Value': 'Standard Grant'}

{'Code': '05050000', 'Directorate': {'Abbreviation': 'CSE', 'LongName': 'Direct For Computer & Info Scie & Enginr'}, 'Division': {'Abbreviation': 'CNS', 'LongName': 'Division Of Computer and Network Systems'}}

{'SignBlockName': 'Dan Cosley', 'PO_EMAI': 'dcosley@nsf.gov', 'PO_PHON': '7032928832'}

This award provides support to 25 US-based graduate students to attend the 2024 Conference on Computer and Communications Security (CCS), to be held Oct 14-18 in Salt Lake City, Utah. CCS is the flagship conference of the Special Interest Group on Security, Audit and Control of the Association for Computing Machinery (ACM). The conference brings together information security researchers, practitioners, developers, and users from all over the world to explore cutting-edge cybersecurity ideas and results. CCS 2024 will include multiple technical tracks across a wide spectrum of cybersecurity topics, with over 240 papers, pre-conference and post-conference workshops, tutorial and poster sessions, and panel discussions. Participation in the conference is a valuable opportunity for students and is an important part of graduate education in cybersecurity.<br/><br/>Students will have the opportunity to observe high-quality presentations and interact with senior researchers in the field both in the main conference and the associated workshops. This can lead to community-based research initiatives, knowledge sharing, and positive social impacts beyond academia. The conference will involve researchers, scholars, and professionals from diverse backgrounds, allowing students to build valuable connections and collaborations. Criteria for selection include evidence of a serious interest in the field as demonstrated by an application letter, a resume showing research outcomes, and/or a support letter from the advising faculty member. The project especially encourages participation from underrepresented groups, to broaden both perspectives and participation in the discipline.<br/><br/>This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

07/25/2024

07/25/2024

None

Grant

47.070

1

4900

4900

2436652

{'FirstName': 'Adwait', 'LastName': 'Nadkarni', 'PI_MID_INIT': None, 'PI_SUFX_NAME': None, 'PI_FULL_NAME': 'Adwait Nadkarni', 'EmailAddress': 'apnadkarni@wm.edu', 'NSF_ID': '000758227', 'StartDate': '07/25/2024', 'EndDate': None, 'RoleCode': 'Principal Investigator'}

{'Name': 'College of William and Mary', 'CityName': 'WILLIAMSBURG', 'ZipCode': '231852817', 'PhoneNumber': '7572213965', 'StreetAddress': '1314 S MOUNT VERNON AVE', 'StreetAddress2': None, 'CountryName': 'United States', 'StateName': 'Virginia', 'StateCode': 'VA', 'CONGRESSDISTRICT': '01', 'CONGRESS_DISTRICT_ORG': 'VA01', 'ORG_UEI_NUM': 'EVWJPCY6AD97', 'ORG_LGL_BUS_NAME': 'COLLEGE OF WILLIAM AND MARY', 'ORG_PRNT_UEI_NUM': None}

{'Name': 'College of William and Mary', 'CityName': 'WILLIAMSBURG', 'StateCode': 'VA', 'ZipCode': '231878795', 'StreetAddress': '1314 S MOUNT VERNON AVE', 'CountryCode': 'US', 'CountryName': 'United States', 'StateName': 'Virginia', 'CountryFlag': '1', 'CONGRESSDISTRICT': '01', 'CONGRESS_DISTRICT_PERF': 'VA01'}

{'Code': '806000', 'Text': 'Secure &Trustworthy Cyberspace'}

2024~25000

{'url': 'https://www.nsf.gov/awardsearch/download?DownloadFileName=2024&All=true', 'xml': '2436652.xml'}

CAREER: Disrupting the Status Quo Regarding Who Gets to be an Engineer

NSF

06/01/2024

02/28/2026

{'Value': 'Continuing Grant'}

{'Code': '07050000', 'Directorate': {'Abbreviation': 'ENG', 'LongName': 'Directorate For Engineering'}, 'Division': {'Abbreviation': 'EEC', 'LongName': 'Div Of Engineering Education and Centers'}}

{'SignBlockName': 'Jesus Soriano Molla', 'PO_EMAI': 'jsoriano@nsf.gov', 'PO_PHON': '7032927795'}

While there has been extensive research on the barriers Black and brown students face as they strive to participate in engineering education and the workforce, there is less scholarship on solutions for addressing this complex challenge. One reason for this is because the scholarship on how change happens in engineering education tends to focus on course content and classroom instruction. Unfortunately, such findings do not easily lend themselves to value-laden, systemic issues like diversity, equity, and inclusion (DEI). Fortunately, some Colleges of Engineering (COEs) throughout the U.S. have adopted change strategies that have resulted in consistently being named among the top-ten producers of Black and brown engineers. This project is motivated by a desire to learn from and follow their example. This CAREER project will disrupt the status quo regarding who gets to be an engineer by investigating five COEs that have significantly changed the face of engineering over the last 20 years. This project will: (1) Advance our understanding of the change strategies that exemplary COEs have used to improve Black and brown students’ access to engineering education and careers; (2) Identify evidence-based models for broadening participation of underrepresented racial/ethnic groups in engineering; and (3) Set COEs on a path to parity, such that the student body demographics in COEs across the country reflect the racial/ethnic makeup of the nation.<br/><br/>Using Kotter’s Leading Change Model and Acker’s Inequality Regimes as a framework, this multi-case study will investigate how exemplary COEs envisioned, implemented, and institutionalized changes that influenced Black and brown students’ access to engineering. The five COEs that will be investigated are: Florida International University, Morgan State University, University of Central Florida, University of Maryland-Baltimore County, and University of Maryland-College Park. Given variations in the types of universities included in the research design, comparing and contrasting insights that emerge from each case will enable the PI to understand the conditions for change. The use of a research study design that relies on both qualitative and quantitative data will produce complementary forms of evidence on what promotes and impedes progress in this context. The research outcomes will include: (1) impact narratives that document concrete examples of how to expand who gets to be an engineer; and (2) a model for broadening participation informed by a cross-case analysis of these exemplars. Furthermore, this timely work focuses on the need to leverage talent from every demographic to diversify the engineering workforce and improve the lived experiences of minoritized groups. The educational outcomes will include: an Impact Playbook that translates the research into actionable strategies; a graduate course for future engineering faculty designed around each of the cases; a townhall discussion among associate professors; sharing insights with ASEE’s Engineering Deans Council; and a partnership with Virginia Tech’s (VT) College of Engineering and College of Science to build capacity among its leaders to envision and enact sustainable changes that promote DEI on VT’s campus. This CAREER project has the potential to reshape how COEs approach their DEI efforts, and increase the likelihood of long-term success. The proposed activities are designed to foster a network of STEM leaders motivated to envision and enact sustainable, scalable changes that expand who gets to be an engineer at their institution.<br/><br/>This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

07/30/2024

07/30/2024

None

Grant

47.041

1

4900

4900

2436663

{'FirstName': 'Jeremi', 'LastName': 'London', 'PI_MID_INIT': 'S', 'PI_SUFX_NAME': None, 'PI_FULL_NAME': 'Jeremi S London', 'EmailAddress': 'jeremi.london@vanderbilt.edu', 'NSF_ID': '000702583', 'StartDate': '07/30/2024', 'EndDate': None, 'RoleCode': 'Principal Investigator'}

{'Name': 'Vanderbilt University', 'CityName': 'NASHVILLE', 'ZipCode': '372032416', 'PhoneNumber': '6153222631', 'StreetAddress': '110 21ST AVE S', 'StreetAddress2': None, 'CountryName': 'United States', 'StateName': 'Tennessee', 'StateCode': 'TN', 'CONGRESSDISTRICT': '05', 'CONGRESS_DISTRICT_ORG': 'TN05', 'ORG_UEI_NUM': 'GTNBNWXJ12D5', 'ORG_LGL_BUS_NAME': 'VANDERBILT UNIVERSITY', 'ORG_PRNT_UEI_NUM': None}

{'Name': 'Vanderbilt University', 'CityName': 'NASHVILLE', 'StateCode': 'TN', 'ZipCode': '372032416', 'StreetAddress': '110 21ST AVE S', 'CountryCode': 'US', 'CountryName': 'United States', 'StateName': 'Tennessee', 'CountryFlag': '1', 'CONGRESSDISTRICT': '05', 'CONGRESS_DISTRICT_PERF': 'TN05'}

[{'Code': '150400', 'Text': 'GOALI-Grnt Opp Acad Lia wIndus'}, {'Code': '768000', 'Text': 'EDA-Eng Diversity Activities'}]

2022~3593

{'url': 'https://www.nsf.gov/awardsearch/download?DownloadFileName=2024&All=true', 'xml': '2436663.xml'}

NSF-SNSF: Past warm climates: Reconciling simulations and proxies

NSF

09/01/2024

08/31/2028

{'Value': 'Standard Grant'}

{'Code': '06040200', 'Directorate': {'Abbreviation': 'GEO', 'LongName': 'Directorate For Geosciences'}, 'Division': {'Abbreviation': 'OCE', 'LongName': 'Division Of Ocean Sciences'}}

{'SignBlockName': 'Gail Christeson', 'PO_EMAI': 'gchriste@nsf.gov', 'PO_PHON': '7032922952'}

The Miocene period (23 to 5 million years ago) had a climate much warmer than modern, with hotter temperatures, less ice at the poles, higher sea level, and greenhouse gas concentrations in the range likely over the next century. Models have had difficulty in reproducing these warm climates. Proxies for past climate are frequently used for comparing against climate models, but their interpretation may be complicated by poor understanding and representation of what environmental conditions they record. The project will evaluate the ability of climate models to predict past climate changes properly. This is important because these are the same models used to project future climate change. The project will be an international collaboration between a US modeling expert and a Swss geochemist expert. Broader impacts include support for a female early-career scientist and outreach activities in both Switzerland and the US. <br/><br/>Climate models have difficulty simulating the weak meridional temperature gradients reconstructed from proxy records of past “greenhouse” climates. These models also normally fail to produce the right globally averaged temperatures when driven by CO2 concentrations reconstructed from proxies. The problem this poses is that: (a) either climate models are missing key physical processes that might be important for future climate prediction or (b) proxy interpretations incorporate deep, persistent biases. The goal of this study is to reconcile climate models and proxy interpretations for an extreme and well-characterized greenhouse climate in the Miocene period (23 to 5 million years ago). The project will use several methods: traditional paleoclimate proxy reconstruction and paleoclimate modeling methods as well as developing novel methods using proxy system models (PSMs). First, a PSM will be developed for coccolithophore-based CO2 and temperature (alkenone and clumped isotope) proxies, and then evaluated using preindustrial climate simulations and corresponding global proxy core top archives. Second, the PSM will be applied to the Miocene simulations. An initial and preliminary evaluation of whether the PSM reduced or improves model-data mismatch compared to traditional methods will be carried out. Third, new orbitally resolved records of phytoplankton isotope fractionation will be built to improve estimates of atmospheric CO2 levels. Additionally, new records of surface ocean temperature in high latitudes and tropical regions will be reconstructed using traditional and PSM methods to test if meridional temperature gradients were consistently flat in the Miocene. Fourth, a series of new Miocene simulations will be conducted using the Community Earth System Model Version 2 with estimated atmospheric CO2 levels, Antarctic ice sheet states and two eccentricity configurations. Finally, the simulated climates will be compared with the proxy results generated by the proxy system model for these Miocene climates. The effort will test the hypothesis that the apparent discrepancy between models and data in the Miocene (and perhaps more generally) is a matter of developing a better understanding of the paleoclimate proxies, not a failure of the climate models. If this hypothesis is invalidated the finger points directly at the climate models, which will be a more solid basis for future work. The results and methods developed in this work will be disseminated broadly and outreach efforts at the youth and K-12 levels will be made both by the Swiss and US teams.<br/><br/>This collaborative U.S.-Swiss project is supported by the U.S. National Science Foundation (NSF) and the Swiss National Science Foundation (SNSF), where NSF funds the U.S. investigator and SNSF funds the partners in Switzerland.<br/><br/>This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

07/11/2024

07/11/2024

None

Grant

47.050

1

4900

4900

2436683

{'FirstName': 'Matthew', 'LastName': 'Huber', 'PI_MID_INIT': None, 'PI_SUFX_NAME': None, 'PI_FULL_NAME': 'Matthew Huber', 'EmailAddress': 'huberm@purdue.edu', 'NSF_ID': '000467824', 'StartDate': '07/11/2024', 'EndDate': None, 'RoleCode': 'Principal Investigator'}

{'Name': 'Purdue University', 'CityName': 'WEST LAFAYETTE', 'ZipCode': '479061332', 'PhoneNumber': '7654941055', 'StreetAddress': '2550 NORTHWESTERN AVE # 1100', 'StreetAddress2': None, 'CountryName': 'United States', 'StateName': 'Indiana', 'StateCode': 'IN', 'CONGRESSDISTRICT': '04', 'CONGRESS_DISTRICT_ORG': 'IN04', 'ORG_UEI_NUM': 'YRXVL4JYCEF5', 'ORG_LGL_BUS_NAME': 'PURDUE UNIVERSITY', 'ORG_PRNT_UEI_NUM': 'YRXVL4JYCEF5'}

{'Name': 'Purdue University', 'CityName': 'WEST LAFAYETTE', 'StateCode': 'IN', 'ZipCode': '479061332', 'StreetAddress': '2550 NORTHWESTERN AVE # 1100', 'CountryCode': 'US', 'CountryName': 'United States', 'StateName': 'Indiana', 'CountryFlag': '1', 'CONGRESSDISTRICT': '04', 'CONGRESS_DISTRICT_PERF': 'IN04'}

{'Code': '162000', 'Text': 'Marine Geology and Geophysics'}

2024~359809

{'url': 'https://www.nsf.gov/awardsearch/download?DownloadFileName=2024&All=true', 'xml': '2436683.xml'}

EAGER: Ion-implanted atom-like defects in semiconductors for single-photon emission at telecommunication wavelengths

NSF

09/01/2024

02/28/2026

{'Value': 'Standard Grant'}

{'Code': '07010000', 'Directorate': {'Abbreviation': 'ENG', 'LongName': 'Directorate For Engineering'}, 'Division': {'Abbreviation': 'ECCS', 'LongName': 'Div Of Electrical, Commun & Cyber Sys'}}

{'SignBlockName': 'Margaret Kim', 'PO_EMAI': 'sekim@nsf.gov', 'PO_PHON': '7032922967'}

Nontechnical Description:<br/>Artificial light with special properties plays a critical role in a number of technologies. Modern communication systems, for example, benefit from the special properties of laser light, which permit low-loss, high-bandwidth, long-distance transmission of information. Ordinary illumination benefits from use of light-emitting diodes in lamps that are brighter and more efficient. This project investigates yet another quest for special properties of light sources: single photon emission. It will investigate deliberately engineered defects in wide bandgap semiconductors; specifically, how to create them so that they behave as isolated single atoms capable of emitting single particles of light at certain chosen wavelengths. Such structures are useful for design of future single-photon emitters. The discrete or particulate nature of light endows it with unique quantum properties that make it useful as a building block for construction of certain classes of quantum computing devices, and for developing special cryptographic hardware for future ultra-secure communication and computing networks. In addition to contributing new knowledge, the project will serve as a training vehicle for a new generation of highly interdisciplinary engineers in quantum information science and technology. <br/><br/>Technical Description: <br/>This effort proposes deterministic creation of atom-like defects in SiC and Al_x Ga_(1-x) N semiconductor films by focused-ion beam implantation at lithographically defined spatial locations. The objective is to implant elements such as vanadium, erbium, and others to form optically active defects that emit at the two low-loss telecommunication wavelengths of 1.3 and1.55 microns. Following post-implantation annealing, the samples will be characterized by rocking curve x-ray diffraction to assess crystallinity. Additional characterization will include spatially resolved micro-photoluminescence. Evidence for quantum light production will be derived from second-order autocorrelation of light emitted by the photoexcited defects. Preliminary feasibility experiments for electrical excitation in specially designed structures with defects in them will also be performed.<br/><br/>This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

08/12/2024

08/12/2024

None

Grant

47.041

1

4900

4900

2436745

{'FirstName': 'Elias', 'LastName': 'Towe', 'PI_MID_INIT': None, 'PI_SUFX_NAME': None, 'PI_FULL_NAME': 'Elias Towe', 'EmailAddress': 'towe@cmu.edu', 'NSF_ID': '000090761', 'StartDate': '08/12/2024', 'EndDate': None, 'RoleCode': 'Principal Investigator'}

{'Name': 'Carnegie-Mellon University', 'CityName': 'PITTSBURGH', 'ZipCode': '152133815', 'PhoneNumber': '4122688746', 'StreetAddress': '5000 FORBES AVE', 'StreetAddress2': None, 'CountryName': 'United States', 'StateName': 'Pennsylvania', 'StateCode': 'PA', 'CONGRESSDISTRICT': '12', 'CONGRESS_DISTRICT_ORG': 'PA12', 'ORG_UEI_NUM': 'U3NKNFLNQ613', 'ORG_LGL_BUS_NAME': 'CARNEGIE MELLON UNIVERSITY', 'ORG_PRNT_UEI_NUM': 'U3NKNFLNQ613'}

{'Name': 'Carnegie-Mellon University', 'CityName': 'PITTSBURGH', 'StateCode': 'PA', 'ZipCode': '152133890', 'StreetAddress': '5000 FORBES AVE', 'CountryCode': 'US', 'CountryName': 'United States', 'StateName': 'Pennsylvania', 'CountryFlag': '1', 'CONGRESSDISTRICT': '12', 'CONGRESS_DISTRICT_PERF': 'PA12'}

{'Code': '151700', 'Text': 'EPMD-ElectrnPhoton&MagnDevices'}

2024~220000

{'url': 'https://www.nsf.gov/awardsearch/download?DownloadFileName=2024&All=true', 'xml': '2436745.xml'}

A Cross-National Study to Promote Mutual Understanding via Social Media Using Generative Artificial Intelligence

NSF

09/15/2024

08/31/2026

{'Value': 'Standard Grant'}

{'Code': '04010000', 'Directorate': {'Abbreviation': 'SBE', 'LongName': 'Direct For Social, Behav & Economic Scie'}, 'Division': {'Abbreviation': 'SMA', 'LongName': 'SBE Off Of Multidisciplinary Activities'}}

{'SignBlockName': 'Antwan Jones', 'PO_EMAI': 'aajones@nsf.gov', 'PO_PHON': '7032924973'}

There is broad concern that social media inhibits productive discussion. Yet, few scholars have explored how social media platforms might be redesigned to counter such trends. This project employs generative artificial intelligence, social simulation models, and online experiments to identify how algorithms that shape the information users see on social media could promote mutual understanding, increase trust, and affect polarization. The project will also develop and, in a laboratory setting, deploy and investigate the consequences of alternative algorithms. Through these comparisons, we hope to reveal how digital platforms can promote mutual understanding and trust, informing future business leaders and policy makers within the industry.<br/><br/>This project employs generative artificial intelligence, agent-based models, and online laboratory experiments to identify how algorithms that shape information users see online influence social norms, trust, and polarization. The first phase of the project trains Large Language Models to simulate social media users by calibrating them with empirical data derived from nationally representative surveys. Preliminary results indicate this research design can reproduce large-scale behaviors on social media platforms, and enable scholars to prototype alternative newsfeed algorithms that lead to different outcomes. The second phase of analyzes the impact of such alternative newsfeed algorithms via randomized controlled trials with human respondents via a tool that enables scholars to build social media platforms for the purposes of scientific research, and allows users to interact with such environments via mobile apps or the web. Embedded surveys will allow the researchers to assess multiple indicators, as well as behavioral data generated by research participants.<br/><br/>This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

08/19/2024

08/19/2024

None

Grant

47.075

1

4900

4900

2436762

{'FirstName': 'Christopher', 'LastName': 'Bail', 'PI_MID_INIT': 'A', 'PI_SUFX_NAME': None, 'PI_FULL_NAME': 'Christopher A Bail', 'EmailAddress': 'christopher.bail@duke.edu', 'NSF_ID': '000637962', 'StartDate': '08/19/2024', 'EndDate': None, 'RoleCode': 'Principal Investigator'}

{'Name': 'Duke University', 'CityName': 'DURHAM', 'ZipCode': '277054640', 'PhoneNumber': '9196843030', 'StreetAddress': '2200 W MAIN ST', 'StreetAddress2': None, 'CountryName': 'United States', 'StateName': 'North Carolina', 'StateCode': 'NC', 'CONGRESSDISTRICT': '04', 'CONGRESS_DISTRICT_ORG': 'NC04', 'ORG_UEI_NUM': 'TP7EK8DZV6N5', 'ORG_LGL_BUS_NAME': 'DUKE UNIVERSITY', 'ORG_PRNT_UEI_NUM': None}

{'Name': 'Duke University', 'CityName': 'DURHAM', 'StateCode': 'NC', 'ZipCode': '277054677', 'StreetAddress': '2200 W MAIN ST', 'CountryCode': 'US', 'CountryName': 'United States', 'StateName': 'North Carolina', 'CountryFlag': '1', 'CONGRESSDISTRICT': '04', 'CONGRESS_DISTRICT_PERF': 'NC04'}

{'Code': '188Y00', 'Text': 'T-AP-Trans-Atlantic Platform'}

2024~200000

{'url': 'https://www.nsf.gov/awardsearch/download?DownloadFileName=2024&All=true', 'xml': '2436762.xml'}

Collaborative Research: FDT-BioTech: First-Principles Informed Data-Enabled Predictive Digital Twin of Human Physiology

NSF

12/15/2024

11/30/2027

{'Value': 'Standard Grant'}

{'Code': '03040000', 'Directorate': {'Abbreviation': 'MPS', 'LongName': 'Direct For Mathematical & Physical Scien'}, 'Division': {'Abbreviation': 'DMS', 'LongName': 'Division Of Mathematical Sciences'}}

{'SignBlockName': 'Yulia Gel', 'PO_EMAI': 'ygel@nsf.gov', 'PO_PHON': '7032927888'}

Digital twins mimic actions and processes of physical assets in real-life executions. This research project concerns with development of a framework for learning digital twins of physical systems capable of incorporating real-world data into first-principles based mathematical representations. Learning digital twin from real data is a novel capability which can help enable effective strategic planning in various domains such as space exploration, autonomous transportation, sustainable water future, smart manufacturing, critical mineral mining, alternative power generation, and healthcare to name a few. This project focuses on applications to important problems in healthcare sciences related to data-informed decision-making exploiting virtual representations of human physiology and has implications for the development and evaluation of new therapies and treatments. One compelling example application is glucose metabolism in people with Type 1 diabetes (T1D). Patients with T1D must replace insulin exogenously as determined by multiple daily measurements of the blood glucose concentration, to maintain glucose homeostasis and avoid hypo / hyper-glycemia and life-threatening diabetic ketoacidosis. As a result, the person with diabetes has to make multiple, complex decisions each day based on food composition, exercise, hormonal cycles and other behavioral factors. Personalized glucose metabolism digital twins developed through this award will be used to devise new ethical treatment modalities and evaluate safety and effectiveness of automated insulin delivery systems without risk factors. Digital twins as such can also feed essential knowledge about system safety and effectiveness to regulatory agencies through assurance cases and advance regulatory science in profound ways. Both study sites University of Houston and Arizona State University are Hispanic serving institutions and the research is integrated with educational and outreach activities to create awareness, especially among youths, and understanding of diabetes and its management, broaden participation of groups traditionally underrepresented in STEM and contribute positively to engineering education. <br/><br/>The first-principles informed data-enabled framework seeks to advance foundational techniques underpinning the development and use of digital twins and synthetic data in biomedical and healthcare domains, by combining advances across mathematical modeling, machine learning (ML) and systems’ theory with human physiology. This research will (1) develop advanced structures based on neural networks (NNs) for the recovery of an underlying physics-based model that are capable of operating in real-world conditions characterized by limited data availability, low and non-uniform sample rate and spatial and temporal noise, (2) develop novel parametrizations of black-box dynamics using NNs from a class of models with "built-in" properties of stability and robustness to perturbations, (3) integrate real-world physical twin generated data which are heterogeneous, scarce and noisy, into its virtual first-principles based mathematical representation, (4) develop a novel framework for learning unmodeled dynamics due to e.g., unaccounted for inputs, inter- and intra-individual variability. Extensive evaluation of this methodology will be conducted using publicly available datasets specially for T1D patients.<br/><br/>This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

08/13/2024

08/13/2024

None

Grant

47.049

1

4900

4900

2436800

{'FirstName': 'Marzia', 'LastName': 'Cescon', 'PI_MID_INIT': None, 'PI_SUFX_NAME': None, 'PI_FULL_NAME': 'Marzia Cescon', 'EmailAddress': 'mcescon2@uh.edu', 'NSF_ID': '000830921', 'StartDate': '08/13/2024', 'EndDate': None, 'RoleCode': 'Principal Investigator'}

{'Name': 'University of Houston', 'CityName': 'HOUSTON', 'ZipCode': '772043067', 'PhoneNumber': '7137435773', 'StreetAddress': '4300 MARTIN LUTHER KING BLVD', 'StreetAddress2': None, 'CountryName': 'United States', 'StateName': 'Texas', 'StateCode': 'TX', 'CONGRESSDISTRICT': '18', 'CONGRESS_DISTRICT_ORG': 'TX18', 'ORG_UEI_NUM': 'QKWEF8XLMTT3', 'ORG_LGL_BUS_NAME': 'UNIVERSITY OF HOUSTON SYSTEM', 'ORG_PRNT_UEI_NUM': None}

{'Name': 'University of Houston', 'CityName': 'HOUSTON', 'StateCode': 'TX', 'ZipCode': '772043067', 'StreetAddress': '4300 MARTIN LUTHER KING BLVD', 'CountryCode': 'US', 'CountryName': 'United States', 'StateName': 'Texas', 'CountryFlag': '1', 'CONGRESSDISTRICT': '18', 'CONGRESS_DISTRICT_PERF': 'TX18'}

[{'Code': '125300', 'Text': 'OFFICE OF MULTIDISCIPLINARY AC'}, {'Code': '745400', 'Text': 'MSPA-INTERDISCIPLINARY'}, {'Code': 'Y18200', 'Text': None}]

2024~370774

{'url': 'https://www.nsf.gov/awardsearch/download?DownloadFileName=2024&All=true', 'xml': '2436800.xml'}

Collaborative Research: FDT-BioTech: First-Principles Informed Data-Enabled Predictive Digital Twin of Human Physiology

NSF

12/15/2024

11/30/2027

{'Value': 'Standard Grant'}

{'Code': '03040000', 'Directorate': {'Abbreviation': 'MPS', 'LongName': 'Direct For Mathematical & Physical Scien'}, 'Division': {'Abbreviation': 'DMS', 'LongName': 'Division Of Mathematical Sciences'}}

{'SignBlockName': 'Yulia Gel', 'PO_EMAI': 'ygel@nsf.gov', 'PO_PHON': '7032927888'}

Digital twins mimic actions and processes of physical assets in real-life executions. This research project concerns with development of a framework for learning digital twins of physical systems capable of incorporating real-world data into first-principles based mathematical representations. Learning digital twin from real data is a novel capability which can help enable effective strategic planning in various domains such as space exploration, autonomous transportation, sustainable water future, smart manufacturing, critical mineral mining, alternative power generation, and healthcare to name a few. This project focuses on applications to important problems in healthcare sciences related to data-informed decision-making exploiting virtual representations of human physiology and has implications for the development and evaluation of new therapies and treatments. One compelling example application is glucose metabolism in people with Type 1 diabetes (T1D). Patients with T1D must replace insulin exogenously as determined by multiple daily measurements of the blood glucose concentration, to maintain glucose homeostasis and avoid hypo / hyper-glycemia and life-threatening diabetic ketoacidosis. As a result, the person with diabetes has to make multiple, complex decisions each day based on food composition, exercise, hormonal cycles and other behavioral factors. Personalized glucose metabolism digital twins developed through this award will be used to devise new ethical treatment modalities and evaluate safety and effectiveness of automated insulin delivery systems without risk factors. Digital twins as such can also feed essential knowledge about system safety and effectiveness to regulatory agencies through assurance cases and advance regulatory science in profound ways. Both study sites University of Houston and Arizona State University are Hispanic serving institutions and the research is integrated with educational and outreach activities to create awareness, especially among youths, and understanding of diabetes and its management, broaden participation of groups traditionally underrepresented in STEM and contribute positively to engineering education. <br/><br/>The first-principles informed data-enabled framework seeks to advance foundational techniques underpinning the development and use of digital twins and synthetic data in biomedical and healthcare domains, by combining advances across mathematical modeling, machine learning (ML) and systems’ theory with human physiology. This research will (1) develop advanced structures based on neural networks (NNs) for the recovery of an underlying physics-based model that are capable of operating in real-world conditions characterized by limited data availability, low and non-uniform sample rate and spatial and temporal noise, (2) develop novel parametrizations of black-box dynamics using NNs from a class of models with "built-in" properties of stability and robustness to perturbations, (3) integrate real-world physical twin generated data which are heterogeneous, scarce and noisy, into its virtual first-principles based mathematical representation, (4) develop a novel framework for learning unmodeled dynamics due to e.g., unaccounted for inputs, inter- and intra-individual variability. Extensive evaluation of this methodology will be conducted using publicly available datasets specially for T1D patients.<br/><br/>This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

08/13/2024

08/13/2024

None

Grant

47.049

1

4900

4900

2436801

[{'FirstName': 'Sandeep', 'LastName': 'Gupta', 'PI_MID_INIT': 'K', 'PI_SUFX_NAME': None, 'PI_FULL_NAME': 'Sandeep K Gupta', 'EmailAddress': 'sandeep.gupta@asu.edu', 'NSF_ID': '000279628', 'StartDate': '08/13/2024', 'EndDate': None, 'RoleCode': 'Principal Investigator'}, {'FirstName': 'Ayan', 'LastName': 'Banerjee', 'PI_MID_INIT': None, 'PI_SUFX_NAME': None, 'PI_FULL_NAME': 'Ayan Banerjee', 'EmailAddress': 'abanerj3@asu.edu', 'NSF_ID': '000685272', 'StartDate': '08/13/2024', 'EndDate': None, 'RoleCode': 'Co-Principal Investigator'}]

{'Name': 'Arizona State University', 'CityName': 'TEMPE', 'ZipCode': '852813670', 'PhoneNumber': '4809655479', 'StreetAddress': '660 S MILL AVENUE STE 204', 'StreetAddress2': None, 'CountryName': 'United States', 'StateName': 'Arizona', 'StateCode': 'AZ', 'CONGRESSDISTRICT': '04', 'CONGRESS_DISTRICT_ORG': 'AZ04', 'ORG_UEI_NUM': 'NTLHJXM55KZ6', 'ORG_LGL_BUS_NAME': 'ARIZONA STATE UNIVERSITY', 'ORG_PRNT_UEI_NUM': None}

{'Name': 'Arizona State University', 'CityName': 'TEMPE', 'StateCode': 'AZ', 'ZipCode': '852813673', 'StreetAddress': '699 S MILL AVENUE STE 553', 'CountryCode': 'US', 'CountryName': 'United States', 'StateName': 'Arizona', 'CountryFlag': '1', 'CONGRESSDISTRICT': '04', 'CONGRESS_DISTRICT_PERF': 'AZ04'}

[{'Code': '125300', 'Text': 'OFFICE OF MULTIDISCIPLINARY AC'}, {'Code': '745400', 'Text': 'MSPA-INTERDISCIPLINARY'}, {'Code': 'Y18200', 'Text': None}]

2024~432000

{'url': 'https://www.nsf.gov/awardsearch/download?DownloadFileName=2024&All=true', 'xml': '2436801.xml'}

CAS: Sextuple Multiredox Organic Compounds for Redox Flow Batteries

NSF

09/01/2024

09/30/2025

{'Value': 'Standard Grant'}

{'Code': '03090000', 'Directorate': {'Abbreviation': 'MPS', 'LongName': 'Direct For Mathematical & Physical Scien'}, 'Division': {'Abbreviation': 'CHE', 'LongName': 'Division Of Chemistry'}}

{'SignBlockName': 'Tingyu Li', 'PO_EMAI': 'tli@nsf.gov', 'PO_PHON': '7032924949'}

In this project, funded by the Chemical Structure, Dynamics & Mechanisms B Program of the Chemistry Division, Xiaoliang Wei of the Department of Mechanical and Energy Engineering at Indiana University-Purdue University Indianapolis is investigating a new family of soluble, stable multi-redox macrocyclic organic (M2O) molecules that undergo reversible six electron electrochemical reactions. Despite the great progress so far, redox flow batteries are still facing critical technical hurdles including low energy density and/or short cycle life. Dr. Wei aims to address these challenges by developing highly soluble M2O molecules capable of six electron transfer with high stability via extended charge delocalization. The goal of this research is to use these molecules to achieve energy-dense, long-life redox flow batteries. Success of this project will increase the deployment capacity of renewable energies and improve the reliability and efficiency of our power grid. This project is highly interdisciplinary combining organic chemistry, electrochemistry, materials science and computational chemistry, and in this way, provides a well-suited platform for scientific training at all levels. Given the nature of the science, this project is expected to engage students considering careers in STEM (science, technology, engineering and medicine) that can benefit society.<br/><br/>This project will explore novel multi-redox macrocyclic organic (M2O) molecules for use in aqueous redox flow batteries to achieve high energy density and long cycle life. The molecular design includes fused heteroaromatic structures with solvatable substituents to provide multi-redox activity, conjugative stabilization, and solubility. The relevant physicochemical and electrochemical properties of synthetic M2O compounds will be correlated with their molecular architectures to unravel the fundamental interplays. The objective of this project is to rationalize the design principles for the development of promising M2O candidates through answering the following questions: (1) What are the mechanisms for establishment of solubility limits and how do the substituents on M2O scaffolds affect the solvation structure and solubility? (2) What are the redox mechanisms of M2O molecules and how do the electrode microstructure affect their redox kinetics? (3) What are the decomposition pathways for unstable M2O molecules and what are the stability-controlling factors?<br/><br/>This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

08/19/2024

08/19/2024

None

Grant

47.049

1

4900

4900

2436843

{'FirstName': 'Xiaoliang', 'LastName': 'Wei', 'PI_MID_INIT': None, 'PI_SUFX_NAME': None, 'PI_FULL_NAME': 'Xiaoliang Wei', 'EmailAddress': 'wei304@purdue.edu', 'NSF_ID': '000786758', 'StartDate': '08/19/2024', 'EndDate': None, 'RoleCode': 'Principal Investigator'}

{'Name': 'Purdue University', 'CityName': 'WEST LAFAYETTE', 'ZipCode': '479061332', 'PhoneNumber': '7654941055', 'StreetAddress': '2550 NORTHWESTERN AVE # 1100', 'StreetAddress2': None, 'CountryName': 'United States', 'StateName': 'Indiana', 'StateCode': 'IN', 'CONGRESSDISTRICT': '04', 'CONGRESS_DISTRICT_ORG': 'IN04', 'ORG_UEI_NUM': 'YRXVL4JYCEF5', 'ORG_LGL_BUS_NAME': 'PURDUE UNIVERSITY', 'ORG_PRNT_UEI_NUM': 'YRXVL4JYCEF5'}

{'Name': 'Purdue University', 'CityName': 'WEST LAFAYETTE', 'StateCode': 'IN', 'ZipCode': '479061332', 'StreetAddress': '2550 NORTHWESTERN AVE # 1100', 'CountryCode': 'US', 'CountryName': 'United States', 'StateName': 'Indiana', 'CountryFlag': '1', 'CONGRESSDISTRICT': '04', 'CONGRESS_DISTRICT_PERF': 'IN04'}

{'Code': '910200', 'Text': 'CMFP-Chem Mech Funct, and Prop'}

2021~80000

{'url': 'https://www.nsf.gov/awardsearch/download?DownloadFileName=2024&All=true', 'xml': '2436843.xml'}

Collaborative Research: Integrating Digitization, Exploration, Genomics, and Student Training to Illuminate Forces Shaping Appalachian Lichen Distributions

NSF

06/01/2024

07/31/2025

{'Value': 'Standard Grant'}

{'Code': '08010000', 'Directorate': {'Abbreviation': 'BIO', 'LongName': 'Direct For Biological Sciences'}, 'Division': {'Abbreviation': 'DEB', 'LongName': 'Division Of Environmental Biology'}}

{'SignBlockName': 'Carolyn J. Ferguson', 'PO_EMAI': 'cferguso@nsf.gov', 'PO_PHON': '7032922689'}

Understanding the distributions of species in nature is a driving theme in the natural sciences, one that has remained elusive for centuries. Multiple forces shape species distributions, including habitat dynamics, species’ ability to move, and species interactions, but the way they contribute on their own and together remains incompletely understood. What we do know about species distributions is based largely on studies of large plants and animals, and processes discovered in those groups may not be the same as for other organisms. This research aims to address fundamental gaps in knowledge of how forces shape distributions of the smaller species that make up the majority of life on Earth. To achieve this the project will focus on lichens (environmentally important fungi that must form stable, permanent interactions with algae to survive) in the Appalachian Mountains of the eastern United States. The Appalachian Mountains are a globally unique and threatened diversity hotspot for many lifeforms, including lichens. The goal of this project is to transform understanding of lichen fungi in a threatened American region while providing vital information for conservation and management of Appalachian ecosystems. A cornerstone of this project is extensive scientific training at all higher education levels, including undergraduate students, graduate students, and a post-doctoral researcher. Authentic, research-based inquiry will be integrated into undergraduate courses, and the developed curriculum will be made publicly available. Outreach and education efforts for the American public will include multiple courses and workshops for professionals and teachers and the Great Appalachian Lichen Bioblitz. Creation of new large-scale, fully integrated, open-access resources for lichen genomics, traits, and Appalachian diversity will improve access to information for diverse audiences including scientists, educators, land managers, and community scientists. <br/><br/>The degree to which forces contribute individually, and through interactions, to mold species distributions is not fully understood, especially for smaller, sessile, symbiotic organisms. This project will use extensive new data resources for lichen species and a comparative population genomics approach for lichens with contrasting distributions to build an integrative understanding of how extrinsic mechanisms and intrinsic biological attributes shape species distributions in the Appalachian Mountain Biodiversity Hotspot. Existing large-scale biodiversity datasets will be integrated with data from a new field inventory to fill the last large Appalachian lichen sampling gap. This will then be used to build comprehensive datasets for lichen distribution size and reproductive traits. Comparative population genomics of species with contrasting distribution sizes will yield datasets for symbiont specificity, gene flow, and adaptation. These data will then collectively be used to test hypotheses that will provide a new perspective on how forces shape species distributions in obligate symbiotic organisms where individuals are non-motile.<br/><br/>This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

07/23/2024

07/23/2024

None

Grant

47.074

1

4900

4900

2436848

{'FirstName': 'James', 'LastName': 'Lendemer', 'PI_MID_INIT': 'C', 'PI_SUFX_NAME': None, 'PI_FULL_NAME': 'James C Lendemer', 'EmailAddress': 'james.lendemer@nysed.gov', 'NSF_ID': '000561945', 'StartDate': '07/23/2024', 'EndDate': None, 'RoleCode': 'Principal Investigator'}

{'Name': 'New York State Education Department', 'CityName': 'ALBANY', 'ZipCode': '122341000', 'PhoneNumber': '5184862423', 'StreetAddress': '89 WASHINGTON AVE', 'StreetAddress2': None, 'CountryName': 'United States', 'StateName': 'New York', 'StateCode': 'NY', 'CONGRESSDISTRICT': '20', 'CONGRESS_DISTRICT_ORG': 'NY20', 'ORG_UEI_NUM': 'M8LLWFW17445', 'ORG_LGL_BUS_NAME': 'NEW YORK STATE EDUCATION DEPARTMENT', 'ORG_PRNT_UEI_NUM': None}

{'Name': 'New York State Education Department', 'CityName': 'ALBANY', 'StateCode': 'NY', 'ZipCode': '122341000', 'StreetAddress': '89 WASHINGTON AVE', 'CountryCode': 'US', 'CountryName': 'United States', 'StateName': 'New York', 'CountryFlag': '1', 'CONGRESSDISTRICT': '20', 'CONGRESS_DISTRICT_PERF': 'NY20'}

{'Code': '737400', 'Text': 'Systematics & Biodiversity Sci'}

2021~406962

{'url': 'https://www.nsf.gov/awardsearch/download?DownloadFileName=2024&All=true', 'xml': '2436848.xml'}

Development of an ultrafine aerosol particle mobility analyzer with separation and sensitivity enhancement and real-time monitoring

NSF

07/01/2024

04/30/2025

{'Value': 'Standard Grant'}

{'Code': '07020000', 'Directorate': {'Abbreviation': 'ENG', 'LongName': 'Directorate For Engineering'}, 'Division': {'Abbreviation': 'CBET', 'LongName': 'Div Of Chem, Bioeng, Env, & Transp Sys'}}

{'SignBlockName': 'Shahab Shojaei-Zadeh', 'PO_EMAI': 'sshojaei@nsf.gov', 'PO_PHON': '7032928045'}

The goal of this project is to develop a new instrument to measure accurately the size distribution of nanoparticles sampled in a variety of environmental applications. The new instrument will enhance the sensitivity, selectivity, and detection speed of ultrafine particles up to 100 nanometers in size, which are referred to as PM0.1 particles. Particles of this small size arise from the same natural or human-made sources that produce larger particles, but PM0.1 particles may pose special health threats because they readily enter the body. Improved measurement techniques could help with the detection and control of PM0.1 particles in the environment. The instrument that will be developed is an Ion Mobility Spectrometer (IMS). The IMS will use an electric field that varies spatially to restrict diffusion of particles in the gas phase and therefore will enhance the resolution of measurements of the particle size distribution. In addition, the IMS system will be coupled to a mass spectrometer so that particle mobility can be correlated with particle mass. The use of this new instrument will improve our fundamental understanding of aerosol and nanoparticle characterization and transport in the gas phase. The research team will conduct activities that demonstrate principles of aerosol science to K-12 students, especially those from underrepresented groups, in summer camps. The team will also communicate the role of aerosols in climate change and pollution through K-12 teacher/mentor awareness symposia.<br/><br/>Aerosols are generally classified by size obtained from their mobility in the gas phase. Most often, mobility-based size distribution functions of aerosol particles are measured with a scanning mobility particle sizer (SMPS). While the SMPS has been highly successful, it has several shortcomings that could be addressed by employing different techniques. For example, diffusional broadening leads to a degradation in resolution for most operating commercial devices. Furthermore, SMPSs typically require minutes to complete voltage scans. This duration limits the information that can be obtained when aerosol samples vary rapidly in time, which can occur when sampling near aircraft or roadways. These challenges are exacerbated for measurements of PM0.1 particles in the gas phase. Despite continued experimental and theoretical interest, there is still a knowledge gap in the theoretical understanding of momentum transfer of particles that lie in the free molecular regime (1-100nm). The proposed research is expected to impact the aerosol field through increases in instrument separation/resolution by restricting diffusion broadening of nanoparticles, classifications of small aerosols through a mass-mobility and size relationship and quick, low signal-to-noise scans to study rapidly varying aerosols (up to tens of milliseconds per scan for particles smaller than 10 nanometers).<br/><br/>This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

08/02/2024

08/02/2024

None

Grant

47.041

1

4900

4900

2436855

{'FirstName': 'Carlos', 'LastName': 'Larriba Andaluz', 'PI_MID_INIT': None, 'PI_SUFX_NAME': None, 'PI_FULL_NAME': 'Carlos Larriba Andaluz', 'EmailAddress': 'clarriba@iupui.edu', 'NSF_ID': '000707331', 'StartDate': '08/02/2024', 'EndDate': None, 'RoleCode': 'Principal Investigator'}

{'Name': 'Purdue University', 'CityName': 'WEST LAFAYETTE', 'ZipCode': '479061332', 'PhoneNumber': '7654941055', 'StreetAddress': '2550 NORTHWESTERN AVE # 1100', 'StreetAddress2': None, 'CountryName': 'United States', 'StateName': 'Indiana', 'StateCode': 'IN', 'CONGRESSDISTRICT': '04', 'CONGRESS_DISTRICT_ORG': 'IN04', 'ORG_UEI_NUM': 'YRXVL4JYCEF5', 'ORG_LGL_BUS_NAME': 'PURDUE UNIVERSITY', 'ORG_PRNT_UEI_NUM': 'YRXVL4JYCEF5'}

{'Name': 'Purdue University', 'CityName': 'WEST LAFAYETTE', 'StateCode': 'IN', 'ZipCode': '479061332', 'StreetAddress': '2550 NORTHWESTERN AVE # 1100', 'CountryCode': 'US', 'CountryName': 'United States', 'StateName': 'Indiana', 'CountryFlag': '1', 'CONGRESSDISTRICT': '04', 'CONGRESS_DISTRICT_PERF': 'IN04'}

{'Code': '141500', 'Text': 'PMP-Particul&MultiphaseProcess'}

2021~72000

{'url': 'https://www.nsf.gov/awardsearch/download?DownloadFileName=2024&All=true', 'xml': '2436855.xml'}

CDS&E: Theoretical, Numerical and Experimental Analysis of Gas-Ion Energy Exchange in Ion Mobility for the Separation of Polyatomic Ions

NSF

08/01/2024

09/30/2026

{'Value': 'Standard Grant'}

{'Code': '03090000', 'Directorate': {'Abbreviation': 'MPS', 'LongName': 'Direct For Mathematical & Physical Scien'}, 'Division': {'Abbreviation': 'CHE', 'LongName': 'Division Of Chemistry'}}

{'SignBlockName': 'Kelsey Cook', 'PO_EMAI': 'kcook@nsf.gov', 'PO_PHON': '7032927490'}

With support from the Chemical Measurement and Imaging Program in the Division of Chemistry, Carlos Larriba-Andaluz and his group at Indiana University-Purdue University in Indianapolis are working on improving the understanding of how ionic compounds may be separated and characterized in the gas phase via a technique known as Ion Mobility Spectrometry (IMS). Understanding how this separation occurs is not only vital to analytical chemistry, but also to aerosol science and plasma physics. One of the reasons that IMS has become quite relevant is that it is able to distinguish ions that have the same mass but different shapes, known as isomers. Distinguishing isomers is extremely important - small changes in shape can result in drastic changes in chemical and physical properties. In fact, IMS systems have arrived at such sophistication that the existing theory is incapable of describing some of the observed separation capabilities. The Larriba-Andaluz group is working to fill this knowledge gap through novel theoretical approaches and numerical tools. The team is engaging undergraduate students, including members of underrepresented groups, through summer courses. They are also developing online IMS training materials to captivate both new and seasoned users.<br/><br/>Ion Mobility Spectrometry (IMS) is becoming one of techniques most employed in combination with Mass Spectrometry (MS). As IMS systems become more sensitive and accurate, commonly used modeling and theory approximations have been unable to explain some recently observed capabilities, including separations of isomers, isotopologues, and even isotopomers. Interchange of energy between translational and internal degrees of freedom accompanying ion-molecule collisions is a potential contributor to these phenomena which has not yet been incorporated into existing theories. The Larriba-Andaluz group is investigating the mechanism and role of this energy exchange under conditions of varying electric field and temperature. Specifically, they are developing 1) higher-order ion mobility approximations using two-temperature theory, followed by the inclusion of ion energy calculations and inelasticity effects, and 2) models of mobility and energy balance using an in-house Molecular Dynamics-Monte Carlo hybrid code. Comparison of experimental results at different fields and temperatures is being used to predict the effect of inelastic contributions. The resulting insights are expected to help explain the remarkable separation ability of ion mobility spectrometry and to broadly advance the field of ion mobility.<br/><br/>This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

08/01/2024

08/01/2024

None

Grant

47.049

1

4900

4900

2436859

{'FirstName': 'Carlos', 'LastName': 'Larriba Andaluz', 'PI_MID_INIT': None, 'PI_SUFX_NAME': None, 'PI_FULL_NAME': 'Carlos Larriba Andaluz', 'EmailAddress': 'clarriba@iupui.edu', 'NSF_ID': '000707331', 'StartDate': '08/01/2024', 'EndDate': None, 'RoleCode': 'Principal Investigator'}

{'Name': 'Purdue University', 'CityName': 'WEST LAFAYETTE', 'ZipCode': '479061332', 'PhoneNumber': '7654941055', 'StreetAddress': '2550 NORTHWESTERN AVE # 1100', 'StreetAddress2': None, 'CountryName': 'United States', 'StateName': 'Indiana', 'StateCode': 'IN', 'CONGRESSDISTRICT': '04', 'CONGRESS_DISTRICT_ORG': 'IN04', 'ORG_UEI_NUM': 'YRXVL4JYCEF5', 'ORG_LGL_BUS_NAME': 'PURDUE UNIVERSITY', 'ORG_PRNT_UEI_NUM': 'YRXVL4JYCEF5'}

{'Name': 'Purdue University', 'CityName': 'WEST LAFAYETTE', 'StateCode': 'IN', 'ZipCode': '479061332', 'StreetAddress': '2550 NORTHWESTERN AVE # 1100', 'CountryCode': 'US', 'CountryName': 'United States', 'StateName': 'Indiana', 'CountryFlag': '1', 'CONGRESSDISTRICT': '04', 'CONGRESS_DISTRICT_PERF': 'IN04'}

{'Code': '688000', 'Text': 'Chemical Measurement & Imaging'}

2022~313000

{'url': 'https://www.nsf.gov/awardsearch/download?DownloadFileName=2024&All=true', 'xml': '2436859.xml'}

I-Corps: Translation Potential of a Co-cultured Cardiomyocyte-on-a-Chip Heart Model Platform

NSF

09/01/2024

08/31/2025

{'Value': 'Standard Grant'}

{'Code': '15030000', 'Directorate': {'Abbreviation': 'TIP', 'LongName': 'Dir for Tech, Innovation, & Partnerships'}, 'Division': {'Abbreviation': 'TI', 'LongName': 'Translational Impacts'}}

{'SignBlockName': 'Ruth Shuman', 'PO_EMAI': 'rshuman@nsf.gov', 'PO_PHON': '7032922160'}

The broader impact of this I-Corps project is the development of a heart muscle cell model platform that can mimic healthy and diseased human heart muscle. Currently, one-third of all new drugs fail due to toxic effects on the heart. This platform is designed to be used to predict how emerging pharmaceutical drugs may interact with the heart. It is known that drugs can change the rate and regularity of the heartbeat and current testing, which is mostly done in animal models, may not be enough to predict toxicity in humans. This technology may benefit pharmaceutical companies that develop and screen muscle-targeting drugs; medical researchers who conduct disease progression, toxicology screenings, and treatment impacts research in universities, research institutes, and pharmaceutical companies; and cardiologists looking for personalized drug screening for their patients. In addition, this model may lower the cost of prescription drugs, serve as a tool for more inclusive research, and impact patient outcomes.<br/><br/>This I-Corps project utilizes experiential learning coupled with a first-hand investigation of the industry ecosystem to assess the translation potential of a cardiomyocyte-on-a-chip drug discovery platform. This benchtop model uses co-cultured human cells designed to simulate healthy and diseased human heart muscle and a piezoelectric material that allows for the measurement of the conversion of the cells’ contractile strength to a corresponding numeric output. This technology reduces manual data analysis time and may result in decreased human bias in the results. In addition, the use of multiple types of human cells makes this a more relevant platform compared to animal models. Testing has demonstrated that the solution may be used for drug toxicity screening, to fill data gaps in disease progression and treatment, and for collecting data representing patient diversity. The technology may provide a quantitative and physiologically relevant solution for non-invasively detecting contractility of cardiac muscle cells to predict how emerging pharmaceutical drugs may interact with the heart.<br/><br/>This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

07/29/2024

07/29/2024

None

Grant

47.084

1

4900

4900

2436874

{'FirstName': 'Kartik', 'LastName': 'Balachandran', 'PI_MID_INIT': None, 'PI_SUFX_NAME': None, 'PI_FULL_NAME': 'Kartik Balachandran', 'EmailAddress': 'kbalacha@uark.edu', 'NSF_ID': '000653267', 'StartDate': '07/29/2024', 'EndDate': None, 'RoleCode': 'Principal Investigator'}

{'Name': 'University of Arkansas', 'CityName': 'FAYETTEVILLE', 'ZipCode': '727013124', 'PhoneNumber': '4795753845', 'StreetAddress': '1125 W MAPLE ST STE 316', 'StreetAddress2': None, 'CountryName': 'United States', 'StateName': 'Arkansas', 'StateCode': 'AR', 'CONGRESSDISTRICT': '03', 'CONGRESS_DISTRICT_ORG': 'AR03', 'ORG_UEI_NUM': 'MECEHTM8DB17', 'ORG_LGL_BUS_NAME': 'UNIVERSITY OF ARKANSAS', 'ORG_PRNT_UEI_NUM': None}

{'Name': 'University of Arkansas', 'CityName': 'FAYETTEVILLE', 'StateCode': 'AR', 'ZipCode': '727011201', 'StreetAddress': '120 John A. White, Jr. Engineering Hall', 'CountryCode': 'US', 'CountryName': 'United States', 'StateName': 'Arkansas', 'CountryFlag': '1', 'CONGRESSDISTRICT': '03', 'CONGRESS_DISTRICT_PERF': 'AR03'}

{'Code': '802300', 'Text': 'I-Corps'}

2024~50000

{'url': 'https://www.nsf.gov/awardsearch/download?DownloadFileName=2024&All=true', 'xml': '2436874.xml'}

Travel: NSF Student Travel Support for the 2024 IEEE International Conference on Data Mining (IEEE ICDM 2024)

NSF

08/01/2024

08/31/2024

{'Value': 'Standard Grant'}

{'Code': '05020000', 'Directorate': {'Abbreviation': 'CSE', 'LongName': 'Direct For Computer & Info Scie & Enginr'}, 'Division': {'Abbreviation': 'IIS', 'LongName': 'Div Of Information & Intelligent Systems'}}

{'SignBlockName': 'Sylvia Spengler', 'PO_EMAI': 'sspengle@nsf.gov', 'PO_PHON': '7032927347'}

The IEEE International Conference on Data Mining (ICDM) has established itself as the world’s premier research conference in data mining. It provides an international forum for the presentation of original research results and the exchange and dissemination of innovative and practical development experiences. The conference covers all aspects of data mining, including algorithms, software, systems, and applications. Student travel awards permit full participation by those who are primary authors on accepted papers. A PhD forum and a Women in Science Research Forum are part of the agenda, and will help early career researchers to learn and exchange cutting-edge research ideas and help them communicate on different aspects of career development.<br/><br/>Data mining and machine learning are now being broadly applied to nearly all disciplines, transforming science, engineering, medicine, healthcare, finance, business, and ultimately society itself. The award will be used to provide travel support for students and early career researchers, with a special focus on women and minorities, for the following activities: 1) To help fund the travel of Ph.D. students who are primary authors of full papers that have been accepted to the technical program; 2) To help fund the travel of Ph.D. students who are participating in the Ph.D. Student Forum; and 3) To help cover the travel expenses of women researchers to participate in the Women in Science Research Forum. This proposal aims to provide the crucial funding needed to support the participation of graduate students and early career researchers who will become future leaders in the science and engineering field. As an effort to engage young researchers, the IEEE ICDM 2024 will involve them in the meeting organization and include mentoring activities in the conference program.<br/><br/>This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

07/11/2024

07/11/2024

None

Grant

47.070

1

4900

4900

2436884

{'FirstName': 'Yi', 'LastName': 'He', 'PI_MID_INIT': None, 'PI_SUFX_NAME': None, 'PI_FULL_NAME': 'Yi He', 'EmailAddress': 'yihe@cs.odu.edu', 'NSF_ID': '000875245', 'StartDate': '07/11/2024', 'EndDate': None, 'RoleCode': 'Principal Investigator'}

{'Name': 'Old Dominion University Research Foundation', 'CityName': 'NORFOLK', 'ZipCode': '235082561', 'PhoneNumber': '7576834293', 'StreetAddress': '4111 MONARCH WAY', 'StreetAddress2': 'STE 204', 'CountryName': 'United States', 'StateName': 'Virginia', 'StateCode': 'VA', 'CONGRESSDISTRICT': '03', 'CONGRESS_DISTRICT_ORG': 'VA03', 'ORG_UEI_NUM': 'DSLXBD7UWRV6', 'ORG_LGL_BUS_NAME': 'OLD DOMINION UNIVERSITY RESEARCH FOUNDATION', 'ORG_PRNT_UEI_NUM': 'DSLXBD7UWRV6'}

{'Name': 'Old Dominion University', 'CityName': 'NORFOLK', 'StateCode': 'VA', 'ZipCode': '235082561', 'StreetAddress': '5115 Hampton Blvd.', 'CountryCode': 'US', 'CountryName': 'United States', 'StateName': 'Virginia', 'CountryFlag': '1', 'CONGRESSDISTRICT': '03', 'CONGRESS_DISTRICT_PERF': 'VA03'}

{'Code': '736400', 'Text': 'Info Integration & Informatics'}

2024~0

{'url': 'https://www.nsf.gov/awardsearch/download?DownloadFileName=2024&All=true', 'xml': '2436884.xml'}

I-Corps: Translation potential of a framework for vehicular cloud computing

NSF

09/01/2024

08/31/2025

{'Value': 'Standard Grant'}

{'Code': '15030000', 'Directorate': {'Abbreviation': 'TIP', 'LongName': 'Dir for Tech, Innovation, & Partnerships'}, 'Division': {'Abbreviation': 'TI', 'LongName': 'Translational Impacts'}}

{'SignBlockName': 'Ruth Shuman', 'PO_EMAI': 'rshuman@nsf.gov', 'PO_PHON': '7032922160'}

The broader impact of this I-Corps project is the development of software to use untapped computing resources in modern cars and other vehicles to provide computational power. Currently, computational power is usually offered via conventional cloud computing services. However, it is possible to extend the cloud computing paradigm using this technology as modern vehicles are equipped with powerful on-board computers and spend numerous hours each day parked in garages, lots, or driveways. The technology leverages the use of vehicles with on-board processors and may open up new avenues for cost-effective and energy-efficient computing solutions. The environmental impact of current and proposed computer server farms has been cited as a limitation to growth of cloud computing. The implementation of a vehicular cloud may reduce the need for large server farms as existing resources are efficiently shared. The vehicular cloud may represent the next stage in on-demand computing. <br/><br/>This I-Corps project utilizes experiential learning coupled with a first-hand investigation of the industry ecosystem to assess the translation potential of a software framework for vehicular cloud computing. The technology specifically provides the means to utilize modern vehicles' onboard computers as computational nodes and is designed to create a software model for vehicular cloud computing. Using this vehicular cloud model, computational resources successively become available and unavailable as cars enter and leave a parking lot. Two key challenges to implementing this technology are to efficiently assign cars to jobs in a dynamic environment, and preserving the confidentiality, integrity, and accessibility of data and services. The stochastic nature of resource availability necessitates the utilization of stochastic modeling techniques and linear programming where applicable. Privacy and security of users and their sensitive information will be ensured by implementing a trust-based authentication model that operates without a central server to monitor vehicle trust scores. This framework may offer unprecedented computing power and has the potential to change the way computational services are delivered.<br/><br/>This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

07/29/2024

07/29/2024

None

Grant

47.084

1

4900

4900

2436894

{'FirstName': 'Puya', 'LastName': 'Ghazizadeh', 'PI_MID_INIT': None, 'PI_SUFX_NAME': None, 'PI_FULL_NAME': 'Puya Ghazizadeh', 'EmailAddress': 'ghazizap@stjohns.edu', 'NSF_ID': '000800266', 'StartDate': '07/29/2024', 'EndDate': None, 'RoleCode': 'Principal Investigator'}

{'Name': "Saint John's University", 'CityName': 'JAMAICA', 'ZipCode': '114399000', 'PhoneNumber': '7189902920', 'StreetAddress': '8000 UTOPIA PKWY', 'StreetAddress2': None, 'CountryName': 'United States', 'StateName': 'New York', 'StateCode': 'NY', 'CONGRESSDISTRICT': '06', 'CONGRESS_DISTRICT_ORG': 'NY06', 'ORG_UEI_NUM': 'QMCKV4B31PG2', 'ORG_LGL_BUS_NAME': "ST JOHN'S UNIVERSITY, NEW YORK", 'ORG_PRNT_UEI_NUM': 'ZNEAYFAHH3H3'}

{'Name': "Saint John's University", 'CityName': 'JAMAICA', 'StateCode': 'NY', 'ZipCode': '114399000', 'StreetAddress': '8000 UTOPIA PKWY', 'CountryCode': 'US', 'CountryName': 'United States', 'StateName': 'New York', 'CountryFlag': '1', 'CONGRESSDISTRICT': '06', 'CONGRESS_DISTRICT_PERF': 'NY06'}

{'Code': '802300', 'Text': 'I-Corps'}

2024~50000

{'url': 'https://www.nsf.gov/awardsearch/download?DownloadFileName=2024&All=true', 'xml': '2436894.xml'}

CAREER: Discovering the Mechanisms Governing Fracture in Fragile Bones

NSF

10/01/2023

03/31/2026

{'Value': 'Standard Grant'}

{'Code': '07030000', 'Directorate': {'Abbreviation': 'ENG', 'LongName': 'Directorate For Engineering'}, 'Division': {'Abbreviation': 'CMMI', 'LongName': 'Div Of Civil, Mechanical, & Manufact Inn'}}

{'SignBlockName': 'Wendy C. Crone', 'PO_EMAI': 'wcrone@nsf.gov', 'PO_PHON': '7032920000'}

This Faculty Early Career Development (CAREER) grant will improve our understanding of the mechanistic origins of fatigue fracture in aged and diabetic fragile bones. This will be achieved by combining low-radiation images of bone damage with mechanical testing of bones and machine learning. This is important because fatigue (cyclic) fracture is a prevalent failure mechanism in nearly all engineered structures, but its relevance to the field of bone tissues has been neglected. These fatigue fractures are common in young athletes, especially in dancers. These fractures are also common in those who have bone fragility. For example, aged and diabetic bones have poor collagen quality and become fragile. Fractures are often thought to be the result of a single overload event, such as a fall. However, this may not explain the cause of all catastrophic fractures because it overlooks the role of fatigue from daily activities. This research project will develop novel dynamic imaging and machine learning for capturing the origins of bone failure mechanisms and associated risk factors. The results will ultimately be used to prevent fragility fractures. This research will provide a transferrable methodological framework for medical imaging, and foster the development of new fracture-resistant materials inspired by biological design principles. The research will be integrated into a long-term educational plan to attract the next generation of female engineers through dance class and other creative learning supports. It is of note that female engineering students in Utah, where this work will be done, are particularly underrepresented in comparison to the rest of the United States. <br/><br/>The specific goal of the research is to advance the development of new bone fracture mechanics theory by using a novel synthesis of synchrotron radiation micro-computed tomography and specific machine learning algorithms to capture the 3D damage evolution during mechanical loading. Previous work has shown that this is typically not achievable by standard synchrotron micro-computed tomography imaging, which involves high radiation doses and causes deterioration of tissue’s mechanical properties. The research work will test the hypothesis that collagen cross-linking accumulation and other diabetic changes in bone quality play an important role in driving fragility fractures. The research tasks of this project include: (i) determine the (sole) effect of collagen cross-linking accumulation on fatigue and fracture resistance ; (ii) evaluate the contribution of collagen cross-linking accumulation in diabetic bone resistance compared with other bone quality factors; (iii) quantify the microscale failure mechanisms in deforming diabetic and crosslinking-rich bones during in situ fatigue and fracture tests; and (iv) evaluate whether cyclic loadings might drive a significant fraction of fractures in diabetic and crosslinking-rich bones. This project can reveal the origins of damage mechanisms in all types of collagenous tissues, and has the potential to lower the radiation level and improve image quality of medical scans. This new knowledge will establish the PI’s long-term career in bone fracture mechanics.<br/><br/>This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

08/05/2024

08/05/2024

None

Grant

47.041

1

4900

4900

2436906

{'FirstName': 'Claire', 'LastName': 'Acevedo', 'PI_MID_INIT': None, 'PI_SUFX_NAME': None, 'PI_FULL_NAME': 'Claire Acevedo', 'EmailAddress': 'cscholtesacevedo@ucsd.edu', 'NSF_ID': '000795683', 'StartDate': '08/05/2024', 'EndDate': None, 'RoleCode': 'Principal Investigator'}

{'Name': 'University of California-San Diego', 'CityName': 'LA JOLLA', 'ZipCode': '920930021', 'PhoneNumber': '8585344896', 'StreetAddress': '9500 GILMAN DR', 'StreetAddress2': None, 'CountryName': 'United States', 'StateName': 'California', 'StateCode': 'CA', 'CONGRESSDISTRICT': '50', 'CONGRESS_DISTRICT_ORG': 'CA50', 'ORG_UEI_NUM': 'UYTTZT6G9DT1', 'ORG_LGL_BUS_NAME': 'UNIVERSITY OF CALIFORNIA, SAN DIEGO', 'ORG_PRNT_UEI_NUM': None}

{'Name': 'University of California-San Diego', 'CityName': 'LA JOLLA', 'StateCode': 'CA', 'ZipCode': '920930021', 'StreetAddress': '9500 GILMAN DR', 'CountryCode': 'US', 'CountryName': 'United States', 'StateName': 'California', 'CountryFlag': '1', 'CONGRESSDISTRICT': '50', 'CONGRESS_DISTRICT_PERF': 'CA50'}

[{'Code': '104500', 'Text': 'CAREER: FACULTY EARLY CAR DEV'}, {'Code': '747900', 'Text': 'BMMB-Biomech & Mechanobiology'}]

['2021~270475', '2022~15840', '2023~15840']

{'url': 'https://www.nsf.gov/awardsearch/download?DownloadFileName=2024&All=true', 'xml': '2436906.xml'}

ERI: A Machine Learning Framework for Preventing Cracking in Semiconductor Materials

NSF

07/01/2024

03/31/2026

{'Value': 'Standard Grant'}

{'Code': '07030000', 'Directorate': {'Abbreviation': 'ENG', 'LongName': 'Directorate For Engineering'}, 'Division': {'Abbreviation': 'CMMI', 'LongName': 'Div Of Civil, Mechanical, & Manufact Inn'}}

{'SignBlockName': 'Siddiq Qidwai', 'PO_EMAI': 'sqidwai@nsf.gov', 'PO_PHON': '7032922211'}

The performance and quality of semiconductor materials are critical to advanced technologies for a wide range of applications. A significant challenge in the production of these materials is the cooling process. During the production phase, semiconductor materials are prone to cracking as they cool. These cracks can lead to failures in the final products, decreased reliability, and higher manufacturing costs. This Engineering Research Initiation (ERI) award supports fundamental research aiming to prevent the formation of cracks during the semiconductor cooling process. The objective of this project is to develop a novel method that integrates machine learning techniques with fundamental principles of mechanics to predict crack formation. This research will enhance production of high-quality semiconductor materials. This project will also make significant contributions to the field of STEM education. A widely accessible Virtual Mechanical Testing Lab will be established, which will use interactive virtual tools to educate students about testing materials. Special efforts will also be made to engage students who have historically been underrepresented in STEM fields in this research.<br/><br/>The goal of this project is to develop a mechanics-informed machine learning framework to predict and quantify interfacial cracking in semiconductor materials, specifically at silicon carbide/aluminum nitride (SiC/AlN) interfaces during the cooling process. Recognizing that interfacial defects and residual stresses are critical factors in cracking, the research aim is to use advanced machine learning and simulation techniques to identify the mechanisms of cracking and proactively prevent it. The machine learning model will be trained using atomistic simulations of cracking behaviors, providing innovative insights into the design of semiconductor materials. The potential contributions of this research are numerous, aiming not only to mitigate damage in semiconductor interfaces, thereby revolutionizing their design and production, but also to develop an integrated machine learning framework with uncertainty quantification, which will have broader applicability in predicting behaviors and properties of other materials.<br/><br/>This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

08/05/2024

08/05/2024

None

Grant

47.041

1

4900

4900

2436919

{'FirstName': 'Shengfeng', 'LastName': 'Yang', 'PI_MID_INIT': None, 'PI_SUFX_NAME': None, 'PI_FULL_NAME': 'Shengfeng Yang', 'EmailAddress': 'shengfengyang@purdue.edu', 'NSF_ID': '000759748', 'StartDate': '08/05/2024', 'EndDate': None, 'RoleCode': 'Principal Investigator'}

{'Name': 'Purdue University', 'CityName': 'WEST LAFAYETTE', 'ZipCode': '479061332', 'PhoneNumber': '7654941055', 'StreetAddress': '2550 NORTHWESTERN AVE # 1100', 'StreetAddress2': None, 'CountryName': 'United States', 'StateName': 'Indiana', 'StateCode': 'IN', 'CONGRESSDISTRICT': '04', 'CONGRESS_DISTRICT_ORG': 'IN04', 'ORG_UEI_NUM': 'YRXVL4JYCEF5', 'ORG_LGL_BUS_NAME': 'PURDUE UNIVERSITY', 'ORG_PRNT_UEI_NUM': 'YRXVL4JYCEF5'}

{'Name': 'Purdue University', 'CityName': 'WEST LAFAYETTE', 'StateCode': 'IN', 'ZipCode': '479061332', 'StreetAddress': '2550 NORTHWESTERN AVE # 1100', 'CountryCode': 'US', 'CountryName': 'United States', 'StateName': 'Indiana', 'CountryFlag': '1', 'CONGRESSDISTRICT': '04', 'CONGRESS_DISTRICT_PERF': 'IN04'}

{'Code': '180Y00', 'Text': 'ERI-Eng. Research Initiation'}

2024~179460

{'url': 'https://www.nsf.gov/awardsearch/download?DownloadFileName=2024&All=true', 'xml': '2436919.xml'}

Emergent Diseases, Patient Activism, and the Co-Production of Expertise and Democracy: A Comparative Study

NSF

09/15/2024

08/31/2027

{'Value': 'Standard Grant'}

{'Code': '04010000', 'Directorate': {'Abbreviation': 'SBE', 'LongName': 'Direct For Social, Behav & Economic Scie'}, 'Division': {'Abbreviation': 'SMA', 'LongName': 'SBE Off Of Multidisciplinary Activities'}}

{'SignBlockName': 'Antwan Jones', 'PO_EMAI': 'aajones@nsf.gov', 'PO_PHON': '7032924973'}

This award will fund a research project to investigate a broad theoretical question: How and to what extent can participatory science counteract the crisis of trust in public health and medical expertise? In recent decades, emergent diseases have caused profound frictions in democratic governance across the Atlantic. Sluggish institutional responses and inadequate treatments have escalated disputes between advocacy groups, patients, medical experts, and scientists in regulatory agencies over the speed, direction, and implications of scientific research. Patient communities in the US and other countries have successfully pushed for more responsive public health policies, and some medical groups have opened themselves to participatory science formats to regain public credibility. Although such cooperatively co-produced expertise holds the potential for counteracting the decline of trust in experts, it is far from clear what formats are best suited to democratize scientific knowledge in ways that do not erode scientific authority and delegitimize expert knowledge. Insights from this project will contribute to deepening and strengthening the dialogue between patients and experts, thereby putting trust in experts on a surer footing. This research will also offer important explanations of how to fortify democratic resilience across the Atlantic in the face of future health crises. <br/> <br/>This project is a comparative study that seeks to document the dynamics of disease advocacy, contestation, and cross-country collaboration. The researchers will also compare across conditions and diseases by adding “control cases.” This comparative framework will allow us to study the co-production of expertise about emergent diseases through archival methods, participant observations, and semi-structured interviews with key stakeholders. In each case, there is a rich tapestry of factors, some of which are local, contextual, and time-dependent, which determine the social character of the parties involved in inclusionary arrangements and the nature of these arrangements. This project contributes to two areas of research in Sociology as well as the field of Science and Technology Studies: (1) a Sociology of Trust in Experts, which will generate innovative research into what makes expertise credible and trustworthy, or on the contrary, mistrusted; and (2) a Sociology of Contested Illnesses, where we will advance critical insights into the dynamics of activism and knowledge in contested illnesses. One of the key deliverables of this project will be to study modes of inclusion in the case studies and to develop an analytical framework that identifies the relations between the various factors, including (1) the organizational format of inclusion; (2) factors shaping the formation of disease identity; (3) the inherited repertoires available to patients; and (4) the political and legal environment.<br/><br/>This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

08/20/2024

08/20/2024

None

Grant

47.075

1

4900

4900

2436930

[{'FirstName': 'Gil', 'LastName': 'Eyal', 'PI_MID_INIT': None, 'PI_SUFX_NAME': None, 'PI_FULL_NAME': 'Gil Eyal', 'EmailAddress': 'ge2027@columbia.edu', 'NSF_ID': '000490537', 'StartDate': '08/20/2024', 'EndDate': None, 'RoleCode': 'Principal Investigator'}, {'FirstName': 'Larry', 'LastName': 'Au', 'PI_MID_INIT': None, 'PI_SUFX_NAME': None, 'PI_FULL_NAME': 'Larry Au', 'EmailAddress': 'lau1@ccny.cuny.edu', 'NSF_ID': '000990901', 'StartDate': '08/20/2024', 'EndDate': None, 'RoleCode': 'Co-Principal Investigator'}, {'FirstName': 'Maya', 'LastName': 'Sabatello', 'PI_MID_INIT': None, 'PI_SUFX_NAME': None, 'PI_FULL_NAME': 'Maya Sabatello', 'EmailAddress': 'ms4075@cumc.columbia.edu', 'NSF_ID': '000991244', 'StartDate': '08/20/2024', 'EndDate': None, 'RoleCode': 'Co-Principal Investigator'}]

{'Name': 'Columbia University', 'CityName': 'NEW YORK', 'ZipCode': '100277922', 'PhoneNumber': '2128546851', 'StreetAddress': '615 W 131ST ST', 'StreetAddress2': 'MC 8741', 'CountryName': 'United States', 'StateName': 'New York', 'StateCode': 'NY', 'CONGRESSDISTRICT': '13', 'CONGRESS_DISTRICT_ORG': 'NY13', 'ORG_UEI_NUM': 'F4N1QNPB95M4', 'ORG_LGL_BUS_NAME': 'THE TRUSTEES OF COLUMBIA UNIVERSITY IN THE CITY OF NEW YORK', 'ORG_PRNT_UEI_NUM': None}

{'Name': 'Columbia University', 'CityName': 'NEW YORK', 'StateCode': 'NY', 'ZipCode': '100277922', 'StreetAddress': '606 W 122nd Street', 'CountryCode': 'US', 'CountryName': 'United States', 'StateName': 'New York', 'CountryFlag': '1', 'CONGRESSDISTRICT': '13', 'CONGRESS_DISTRICT_PERF': 'NY13'}

{'Code': '188Y00', 'Text': 'T-AP-Trans-Atlantic Platform'}

2024~199097

{'url': 'https://www.nsf.gov/awardsearch/download?DownloadFileName=2024&All=true', 'xml': '2436930.xml'}

Conference: Ninth Physics and Astrophysics at the eXtreme (PAX IX) Workshop

NSF

08/01/2024

07/31/2025

{'Value': 'Standard Grant'}

{'Code': '03010000', 'Directorate': {'Abbreviation': 'MPS', 'LongName': 'Direct For Mathematical & Physical Scien'}, 'Division': {'Abbreviation': 'PHY', 'LongName': 'Division Of Physics'}}

{'SignBlockName': 'Pedro Marronetti', 'PO_EMAI': 'pmarrone@nsf.gov', 'PO_PHON': '7032927372'}

This award supports attendance at the 9th Physics At eXtreme (PAX) workshop, focusing entirely on next-generation gravitational-wave science opportunities and challenges. PAX is a discussion-driven workshop and includes experts from a wide range of fields that will benefit from next-generation detectors: astronomers, astrophysicists, general relativity theorists, cosmologists, nuclear physicists, numerical relativists, and data analysts. PAX provides the optimal venue to identify actionable items and initiate collaborations to tackle the work needed to maximize the scientific output of the next-generation detectors.<br/><br/>Next-generation detectors will be built in the second half of the 2030s when the current generation of students will lead the field. A major goal of PAX is to directly and actively involve early career scientists in shaping the future of the field. The 9th PAX workshop will include an early career scientist lunch where young and experienced researchers can discuss how to make academia more accessible and equitable.<br/><br/>This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

07/26/2024

07/26/2024

None

Grant

47.049

1

4900

4900

2436939

{'FirstName': 'Bangalore', 'LastName': 'Sathyaprakash', 'PI_MID_INIT': 'S', 'PI_SUFX_NAME': None, 'PI_FULL_NAME': 'Bangalore S Sathyaprakash', 'EmailAddress': 'bss25@psu.edu', 'NSF_ID': '000733569', 'StartDate': '07/26/2024', 'EndDate': None, 'RoleCode': 'Principal Investigator'}

{'Name': 'Pennsylvania State Univ University Park', 'CityName': 'UNIVERSITY PARK', 'ZipCode': '168021503', 'PhoneNumber': '8148651372', 'StreetAddress': '201 OLD MAIN', 'StreetAddress2': None, 'CountryName': 'United States', 'StateName': 'Pennsylvania', 'StateCode': 'PA', 'CONGRESSDISTRICT': '15', 'CONGRESS_DISTRICT_ORG': 'PA15', 'ORG_UEI_NUM': 'NPM2J7MSCF61', 'ORG_LGL_BUS_NAME': 'THE PENNSYLVANIA STATE UNIVERSITY', 'ORG_PRNT_UEI_NUM': None}

{'Name': 'Pennsylvania State Univ University Park', 'CityName': 'UNIVERSITY PARK', 'StateCode': 'PA', 'ZipCode': '168021503', 'StreetAddress': '201 OLD MAIN', 'CountryCode': 'US', 'CountryName': 'United States', 'StateName': 'Pennsylvania', 'CountryFlag': '1', 'CONGRESSDISTRICT': '15', 'CONGRESS_DISTRICT_PERF': 'PA15'}

{'Code': '124300', 'Text': 'Gravity Exp. & Data Analysis'}

2024~13809

{'url': 'https://www.nsf.gov/awardsearch/download?DownloadFileName=2024&All=true', 'xml': '2436939.xml'}

Belonging in Geoscience Education Workshop: Planning to Enact Cultural Change

NSF

10/01/2024

09/30/2025

{'Value': 'Standard Grant'}

{'Code': '11040000', 'Directorate': {'Abbreviation': 'EDU', 'LongName': 'Directorate for STEM Education'}, 'Division': {'Abbreviation': 'DUE', 'LongName': 'Division Of Undergraduate Education'}}

{'SignBlockName': 'Keith Sverdrup', 'PO_EMAI': 'ksverdru@nsf.gov', 'PO_PHON': '7032924671'}

This project aims to serve the national interest by recognizing, characterizing, and understanding professional development activities and strategies that will lead to positive cultural change and increased sense of belonging for individuals in the geosciences. This workshop will support the evolution of geoscience professional development programming, maximizing its utility and reach in meeting the current challenges related to broadening of opportunities and expanding participation of groups, institutions, and geographic regions that are underrepresented in the geosciences. It is designed to gain insight into ways that the National Association of Geoscience Teacher’s On the Cutting Edge, an existing geoscience education community of practice, can evolve professional development programming to be current and relevant and encourage leadership in the inclusive teaching space. Invited participants will represent a range of backgrounds and experience and include individuals (1) from groups that are underrepresented in the current community and (2) who serve as change agents through NSF-funded initiatives and programs. Planned workshop activities will support robust generation and synthesis of ideas and recommendations to guide future efforts.<br/><br/>The goal of this workshop is to begin the work of mapping out new directions for future professional development based on the lived experiences of geoscientists from historically excluded groups, as well as those who have experience working in institutional change in geoscience education. The workshop program and structure will employ best practices learned from the successful implementation of previous geoscience education professional development activities to convene an interactive workshop and provide significant opportunities for discussion and synthesis among participants. Discussions will be structured to elicit ideas and recommendations in each of four target areas 1) departmental culture 2) creating inclusive spaces 3) programmatic support for change efforts and 4) infrastructure support for sustaining programming. Recommendations produced during this workshop will guide future development and adaptation of sustainable programs to better cultivate a thriving, diverse geoscience community, and to increase valued programming for geoscience education research in creating a culture of belonging. The resulting synthesis and recommendations will be shared publicly and made available to practitioners across STEM fields. The NSF IUSE: EDU Program supports research and development projects to improve the effectiveness of STEM education for all students.<br/><br/>This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

08/21/2024

08/21/2024

None

Grant

47.076

1

4900

4900

2436940

{'FirstName': 'Cailin', 'LastName': 'Orr', 'PI_MID_INIT': 'H', 'PI_SUFX_NAME': None, 'PI_FULL_NAME': 'Cailin H Orr', 'EmailAddress': 'corr@carleton.edu', 'NSF_ID': '000524550', 'StartDate': '08/21/2024', 'EndDate': None, 'RoleCode': 'Principal Investigator'}

{'Name': 'Carleton College', 'CityName': 'NORTHFIELD', 'ZipCode': '550574001', 'PhoneNumber': '5072224303', 'StreetAddress': '1 N COLLEGE ST', 'StreetAddress2': None, 'CountryName': 'United States', 'StateName': 'Minnesota', 'StateCode': 'MN', 'CONGRESSDISTRICT': '02', 'CONGRESS_DISTRICT_ORG': 'MN02', 'ORG_UEI_NUM': 'KALKKJL418Q7', 'ORG_LGL_BUS_NAME': 'CARLETON COLLEGE', 'ORG_PRNT_UEI_NUM': None}

{'Name': 'Carleton College', 'CityName': 'NORTHFIELD', 'StateCode': 'MN', 'ZipCode': '550574001', 'StreetAddress': '1 N COLLEGE ST', 'CountryCode': 'US', 'CountryName': 'United States', 'StateName': 'Minnesota', 'CountryFlag': '1', 'CONGRESSDISTRICT': '02', 'CONGRESS_DISTRICT_PERF': 'MN02'}

{'Code': '199800', 'Text': 'IUSE'}

2024~49920

{'url': 'https://www.nsf.gov/awardsearch/download?DownloadFileName=2024&All=true', 'xml': '2436940.xml'}

Conference: Cnidofest: A workshop on cnidarian model organism biology, August 14th-17th, Bethlehem, PA

NSF

07/01/2024

06/30/2025

{'Value': 'Standard Grant'}

{'Code': '08090000', 'Directorate': {'Abbreviation': 'BIO', 'LongName': 'Direct For Biological Sciences'}, 'Division': {'Abbreviation': 'IOS', 'LongName': 'Division Of Integrative Organismal Systems'}}

{'SignBlockName': 'Anna Allen', 'PO_EMAI': 'akallen@nsf.gov', 'PO_PHON': '7032928011'}

The Cnidarian Model Systems Meeting, or "Cnidofest," is a biennial conference that brings together researchers studying cnidarians, a group of animals that includes jellyfish, corals, and sea anemones. These animals have several features of scientific interest, including their importance to ocean health, for example coral reef habitats, and their unique biology, for example their extreme regenerative abilities and diverse forms. Cnidofest 2024 is the third iteration of this meeting and is taking place at Lehigh University. The first Cnidofest was held in 2018 at the University of Florida and the second in 2022 at the University of California, Davis after taking 2020 off due to the pandemic. This growing conference emphasizes the importance of bringing together scientists of all career stages. The conference prioritizes trainee involvement and networking, with trainees giving 75% of the oral presentations. Through the support of trainees, Cnidofest is committed to expanding the community, both in total numbers and in diversity. In particular, expanding opportunities for researchers from diverse backgrounds as well as researchers using diverse model systems is a high priority. Therefore, efforts are made to keep costs low and provide financial support for trainees, ensuring broad participation. Cnidofest also showcases cutting-edge technologies, helping researchers integrate new tools into their research. These talks are given by researchers outside of the community to enable new perspectives. Overall, Cnidofest supports the growth and advancement of the cnidarian research community and is vital component of their success.<br/><br/>Cnidarian laboratory models have been used to make fundamental discoveries, including in neurobiology, developmental biology, ecology, evolution, and symbiosis. However, this group of organisms have been historically understudied due to technology limitations. In the past several years, this has changed due to advances in genomics and gene manipulation technologies that can now be easily applied to diverse animals. The advantages of cnidarians for laboratory research include: 1) Simple, well-understood body plans with highly complex and variable life cycles. 2) An informative phylogenetic position as the clade that is sister to bilaterians; discoveries made in cnidarians often uncover deeply conserved processes. 3) Transparency makes them amenable to live imaging. 4) Interesting biology such as self/non-self recognition, the study of algal symbioses, such as found in reef-building corals, and extreme regenerative abilities. The Cnidarian Model Systems Meeting, or "Cnidofest 2024," will bring together researchers studying diverse cnidarians and will emphasize new technological approaches to enhance cnidarian research. Three technology speakers will present seminars on emerging technologies 1) Spatial Transcriptomics, 2) Expansion Microscopy, and 3) Optogenetics. Interactions among participants will be encouraged through a schedule that includes oral presentations, lightning talks, and poster presentations. In addition, time is built into the schedule for informal discussions over breaks and meals, which are all done as one group. The goal is to exchange ideas for advancing research in cnidarians, as well as foster growth in the community by supporting trainee meeting costs.<br/><br/>This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

07/01/2024

07/01/2024

None

Grant

47.074

1

4900

4900

2436941

[{'FirstName': 'Michael', 'LastName': 'Layden', 'PI_MID_INIT': 'J', 'PI_SUFX_NAME': None, 'PI_FULL_NAME': 'Michael J Layden', 'EmailAddress': 'mjl514@lehigh.edu', 'NSF_ID': '000688775', 'StartDate': '07/01/2024', 'EndDate': None, 'RoleCode': 'Principal Investigator'}, {'FirstName': 'Celina', 'LastName': 'Juliano', 'PI_MID_INIT': None, 'PI_SUFX_NAME': None, 'PI_FULL_NAME': 'Celina Juliano', 'EmailAddress': 'cejuliano@ucdavis.edu', 'NSF_ID': '000716471', 'StartDate': '07/01/2024', 'EndDate': None, 'RoleCode': 'Co-Principal Investigator'}]

{'Name': 'Lehigh University', 'CityName': 'BETHLEHEM', 'ZipCode': '180153008', 'PhoneNumber': '6107583021', 'StreetAddress': '526 BRODHEAD AVE', 'StreetAddress2': None, 'CountryName': 'United States', 'StateName': 'Pennsylvania', 'StateCode': 'PA', 'CONGRESSDISTRICT': '07', 'CONGRESS_DISTRICT_ORG': 'PA07', 'ORG_UEI_NUM': 'E13MDBKHLDB5', 'ORG_LGL_BUS_NAME': 'LEHIGH UNIVERSITY', 'ORG_PRNT_UEI_NUM': None}

{'Name': 'Lehigh University', 'CityName': 'BETHLEHEM', 'StateCode': 'PA', 'ZipCode': '180153008', 'StreetAddress': '526 BRODHEAD AVE', 'CountryCode': 'US', 'CountryName': 'United States', 'StateName': 'Pennsylvania', 'CountryFlag': '1', 'CONGRESSDISTRICT': '07', 'CONGRESS_DISTRICT_PERF': 'PA07'}

{'Code': '111900', 'Text': 'Animal Developmental Mechanism'}

2024~20000

{'url': 'https://www.nsf.gov/awardsearch/download?DownloadFileName=2024&All=true', 'xml': '2436941.xml'}

I-Corps: Translation Potential of Using Artificial Intelligence and Machine Learning to Detect Violent Motivation Online

NSF

09/01/2024

02/28/2025

{'Value': 'Standard Grant'}

{'Code': '15030000', 'Directorate': {'Abbreviation': 'TIP', 'LongName': 'Dir for Tech, Innovation, & Partnerships'}, 'Division': {'Abbreviation': 'TI', 'LongName': 'Translational Impacts'}}

{'SignBlockName': 'Molly Wasko', 'PO_EMAI': 'mwasko@nsf.gov', 'PO_PHON': '7032924749'}

The broader impact of this I-Corps project is based on the development of an artificial intelligence technology to enhance the efficiency and effectiveness of online security measures. The technology analyzes emotional weighting in natural language to detect violent motivations within social media content in real-time. By identifying violent intentions early, the goal is to prevent harm and protect individuals and communities. Real-time analysis also has the potential to enhance safety and security, enabling law enforcement agencies and security personnel to respond swiftly to threats. Social media platforms can use this technology to automatically flag and remove harmful content, maintaining a safer online environment. Lastly, identifying violent language can also help direct users to mental health resources or crisis intervention services. This solution could improve how security threats are identified and managed and provide a scalable solution to address the pressing need for improved social media security while contributing to a safer digital space by proactively addressing violent motivations.<br/><br/>This I-Corps project utilizes experiential learning coupled with a first-hand investigation of the industry ecosystem to assess the translation potential of the technology. The solution is based on research that identifies the moral and emotional motivations that drive violent behavior. Previous research demonstrates accurate detection of users with strong moral motivations and their intended targets, thus the ability to identify violent actors via these specific motivators. This technology is based on an artificial intelligence (AI)-driven solution to detect nuanced indicators of violent motivation online. By analyzing text, images, and videos, this technology goes beyond traditional sentiment analysis, creating a more proactive approach to detecting the propensity for violent behavior online and deterring actual violent behavior in the real-world. By developing an application that analyzes social media content based on these research findings, this solution could address a critically important gap in current social media security measures.<br/><br/>This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

07/29/2024

07/29/2024

None

Grant

47.084

1

4900

4900

2436966

{'FirstName': 'Lindsay', 'LastName': 'Hahn', 'PI_MID_INIT': None, 'PI_SUFX_NAME': None, 'PI_FULL_NAME': 'Lindsay Hahn', 'EmailAddress': 'Lhahn2@buffalo.edu', 'NSF_ID': '0000A0DSF', 'StartDate': '07/29/2024', 'EndDate': None, 'RoleCode': 'Principal Investigator'}

{'Name': 'SUNY at Buffalo', 'CityName': 'AMHERST', 'ZipCode': '142282577', 'PhoneNumber': '7166452634', 'StreetAddress': '520 LEE ENTRANCE STE 211', 'StreetAddress2': None, 'CountryName': 'United States', 'StateName': 'New York', 'StateCode': 'NY', 'CONGRESSDISTRICT': '26', 'CONGRESS_DISTRICT_ORG': 'NY26', 'ORG_UEI_NUM': 'LMCJKRFW5R81', 'ORG_LGL_BUS_NAME': 'RESEARCH FOUNDATION FOR THE STATE UNIVERSITY OF NEW YORK, THE', 'ORG_PRNT_UEI_NUM': 'GMZUKXFDJMA9'}

{'Name': 'SUNY at Buffalo', 'CityName': 'AMHERST', 'StateCode': 'NY', 'ZipCode': '142601000', 'StreetAddress': '353 Baldy Hall', 'CountryCode': 'US', 'CountryName': 'United States', 'StateName': 'New York', 'CountryFlag': '1', 'CONGRESSDISTRICT': '26', 'CONGRESS_DISTRICT_PERF': 'NY26'}

{'Code': '802300', 'Text': 'I-Corps'}

2024~50000

{'url': 'https://www.nsf.gov/awardsearch/download?DownloadFileName=2024&All=true', 'xml': '2436966.xml'}

A Comparative Study of Migrant Communities

NSF

09/15/2024

08/31/2026

{'Value': 'Standard Grant'}

{'Code': '04010000', 'Directorate': {'Abbreviation': 'SBE', 'LongName': 'Direct For Social, Behav & Economic Scie'}, 'Division': {'Abbreviation': 'SMA', 'LongName': 'SBE Off Of Multidisciplinary Activities'}}

{'SignBlockName': 'James I. Walsh', 'PO_EMAI': 'jwalsh@nsf.gov', 'PO_PHON': '7032924689'}

This award will fund a research project that studies the dynamics between migration and democracy. It explores the challenges and opportunities posed to governance, political inclusion, and cooperation. Areas of investigation include the role of émigrés in fostering resistance and the development of civil society networks. The major questions being investigated are how political migrants interact with the political landscapes of their host countries and their homeland; how these exiles contribute to political dynamics in their homeland through political remittances; and how the narrative of political emigration is leveraged to shape perceptions in both sending and receiving countries. This research spans democratization studies, international relations, migration studies, and media studies.<br/><br/>This award will fund a research project that employs a mixed-methods approach using a combination of online surveys, semi-structured interviews, focus groups, and automated text analysis of media to collect and analyze data to study the effects of political emigration to countries on politics of the countries these emigres left behind. Survey data will be collected on at least 4,000 observations of political migrants. The two rounds of online surveys will explore their lived situations and political behavior. This approach will track shifts in their attitudes toward political engagement, political activism, dynamics of their relationships with host countries’ communities and governments, and influence on political discourse in their home country. The qualitative methods involve conducting 100 in-depth interviews with emigrants. Additionally, interviews with returnees will take place and focus groups will examine media consumption and will be held with political migrants to assess the effectiveness of narratives in international media.<br/><br/>This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

08/19/2024

08/19/2024

None

Grant

47.075

1

4900

4900

2436968

[{'FirstName': 'Ivetta', 'LastName': 'Sergeeva', 'PI_MID_INIT': None, 'PI_SUFX_NAME': None, 'PI_FULL_NAME': 'Ivetta Sergeeva', 'EmailAddress': 'ivetta.sergeeva@gwu.edu', 'NSF_ID': '0000A0G2M', 'StartDate': '08/19/2024', 'EndDate': None, 'RoleCode': 'Co-Principal Investigator'}, {'FirstName': 'Marlene', 'LastName': 'Laruelle', 'PI_MID_INIT': None, 'PI_SUFX_NAME': None, 'PI_FULL_NAME': 'Marlene Laruelle', 'EmailAddress': 'laruelle@gwu.edu', 'NSF_ID': '000599863', 'StartDate': '08/19/2024', 'EndDate': None, 'RoleCode': 'Principal Investigator'}]

{'Name': 'George Washington University', 'CityName': 'WASHINGTON', 'ZipCode': '200520042', 'PhoneNumber': '2029940728', 'StreetAddress': '1918 F ST NW', 'StreetAddress2': None, 'CountryName': 'United States', 'StateName': 'District of Columbia', 'StateCode': 'DC', 'CONGRESSDISTRICT': '00', 'CONGRESS_DISTRICT_ORG': 'DC00', 'ORG_UEI_NUM': 'ECR5E2LU5BL6', 'ORG_LGL_BUS_NAME': 'GEORGE WASHINGTON UNIVERSITY (THE)', 'ORG_PRNT_UEI_NUM': None}

{'Name': 'George Washington University', 'CityName': 'WASHINGTON', 'StateCode': 'DC', 'ZipCode': '200520042', 'StreetAddress': '1918 F ST NW', 'CountryCode': 'US', 'CountryName': 'United States', 'StateName': 'District of Columbia', 'CountryFlag': '1', 'CONGRESSDISTRICT': '00', 'CONGRESS_DISTRICT_PERF': 'DC00'}

{'Code': '188Y00', 'Text': 'T-AP-Trans-Atlantic Platform'}

2024~200000

{'url': 'https://www.nsf.gov/awardsearch/download?DownloadFileName=2024&All=true', 'xml': '2436968.xml'}

Planning: CRISES: Science Communication for Resilient Environments and Societies (SCORES)

NSF

09/15/2024

08/31/2025

{'Value': 'Standard Grant'}

{'Code': '04010000', 'Directorate': {'Abbreviation': 'SBE', 'LongName': 'Direct For Social, Behav & Economic Scie'}, 'Division': {'Abbreviation': 'SMA', 'LongName': 'SBE Off Of Multidisciplinary Activities'}}

{'SignBlockName': 'Tom Evans', 'PO_EMAI': 'tevans@nsf.gov', 'PO_PHON': '7032924891'}

Hazards such as wildfires and landslides threaten environmental and societal resilience and increase systemic inequities. Although experts provide increasingly accurate and speedy communication to guide public actions in response to hazard events, public actions frequently do not align with the best available science and guidance. Expert intuitions about how to communicate also do not always match evidence-based practices and, indeed, can harm comprehension and use of information. Thus, the need exists to understand how people judge threats and make decisions about them so that more effective science communication methods are developed. This planning project investigates what is currently known and not known in these areas and identifies research gaps and opportunities that advance community needs. Developing long-term capacity to deliver effective risk messages will empower people to act safely in response to threats, build a more resilient society, and enhance quality of life. Ultimately, it will improve societal wellbeing by increasing the public’s autonomy, avoiding harms of not preparing for crises or reacting inappropriately to them as they occur, improving quality of life, and saving money and lives. <br/><br/>This planning project develops plans for a transdisciplinary, convergent, and collaborative approach to advance knowledge on science communication related to environmental hazards. The project collaborates with historically marginalized and vulnerable communities to identify key opportunities and challenges drawing from research in decision science, risk analysis, psychology, and science communication. The project uses a mixed methods and solutions-oriented approach that considers people’s mental models of hazards—their psychological representations of how hazards work and have impact—to develop evidence-based messages and educate communities about hazards while motivating protective behaviors. It integrates theory on mental models with that on emotion and statistics, thus contributing to mental-model, risk-communication, and other theories. Planning and convening activities enable an integrative assessment from a social behavioral framework and co-produce a compelling and responsive strategic plan that captures the current landscape of research, identifies research gaps and opportunities—especially with respect to decision, risk, and psychological sciences related to hazards—aligns with and advances community needs, and leverages and develops natural and social sciences. The emergent theory-based taxonomy of effective risk communication is expected to improve hazard decision making.<br/><br/>This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

08/21/2024

08/21/2024

None

Grant

47.075

1

4900

4900

2436970

[{'FirstName': 'Dare', 'LastName': 'Baldwin', 'PI_MID_INIT': 'A', 'PI_SUFX_NAME': None, 'PI_FULL_NAME': 'Dare A Baldwin', 'EmailAddress': 'baldwin@uoregon.edu', 'NSF_ID': '000278806', 'StartDate': '08/21/2024', 'EndDate': None, 'RoleCode': 'Co-Principal Investigator'}, {'FirstName': 'Ellen', 'LastName': 'Peters', 'PI_MID_INIT': None, 'PI_SUFX_NAME': None, 'PI_FULL_NAME': 'Ellen Peters', 'EmailAddress': 'ellenpet@uoregon.edu', 'NSF_ID': '000329592', 'StartDate': '08/21/2024', 'EndDate': None, 'RoleCode': 'Principal Investigator'}, {'FirstName': 'Lucas', 'LastName': 'Silva', 'PI_MID_INIT': None, 'PI_SUFX_NAME': None, 'PI_FULL_NAME': 'Lucas Silva', 'EmailAddress': 'lsilva7@uoregon.edu', 'NSF_ID': '000605350', 'StartDate': '08/21/2024', 'EndDate': None, 'RoleCode': 'Co-Principal Investigator'}, {'FirstName': 'Valerie', 'LastName': 'Sahakian', 'PI_MID_INIT': 'J', 'PI_SUFX_NAME': None, 'PI_FULL_NAME': 'Valerie J Sahakian', 'EmailAddress': 'vjs@uoregon.edu', 'NSF_ID': '000780133', 'StartDate': '08/21/2024', 'EndDate': None, 'RoleCode': 'Co-Principal Investigator'}]

{'Name': 'University of Oregon Eugene', 'CityName': 'EUGENE', 'ZipCode': '974031905', 'PhoneNumber': '5413465131', 'StreetAddress': '1776 E 13TH AVE', 'StreetAddress2': None, 'CountryName': 'United States', 'StateName': 'Oregon', 'StateCode': 'OR', 'CONGRESSDISTRICT': '04', 'CONGRESS_DISTRICT_ORG': 'OR04', 'ORG_UEI_NUM': 'Z3FGN9MF92U2', 'ORG_LGL_BUS_NAME': 'UNIVERSITY OF OREGON', 'ORG_PRNT_UEI_NUM': 'Z3FGN9MF92U2'}

{'Name': 'University of Oregon Eugene', 'CityName': 'EUGENE', 'StateCode': 'OR', 'ZipCode': '974031905', 'StreetAddress': '1776 E 13TH AVE', 'CountryCode': 'US', 'CountryName': 'United States', 'StateName': 'Oregon', 'CountryFlag': '1', 'CONGRESSDISTRICT': '04', 'CONGRESS_DISTRICT_PERF': 'OR04'}

{'Code': '265Y00', 'Text': 'CRISES-R&I in Sci, Env&Society'}

2024~100000

{'url': 'https://www.nsf.gov/awardsearch/download?DownloadFileName=2024&All=true', 'xml': '2436970.xml'}

DTAP: Balancing Trust and Accountability: Charities, Government, and Society

NSF

09/15/2024

08/31/2027

{'Value': 'Standard Grant'}

{'Code': '04010000', 'Directorate': {'Abbreviation': 'SBE', 'LongName': 'Direct For Social, Behav & Economic Scie'}, 'Division': {'Abbreviation': 'SMA', 'LongName': 'SBE Off Of Multidisciplinary Activities'}}

{'SignBlockName': 'Antwan Jones', 'PO_EMAI': 'aajones@nsf.gov', 'PO_PHON': '7032924973'}

Nonprofit organizations play multiple and sometimes conflicting roles in a democracy: they represent many views but can also provide services at the direction of government. Public trust in charities has decreased over recent years in many countries, but little research explains why. This project utilizes The Trans-Atlantic Partnership call on Democracy, Governance and Trust (DGT) to study cross-sector opinions on trust and accountability. The research team seeks to understand the variation in regulatory approaches, interpersonal trust, and popular sentiment toward public-serving institutions using the mutual perceptions of four audiences: operating charities, foundations, government agencies, and the public. Findings will provide useful guidance for regulators, charity workers, and donors on the broader role of regulation and democratic participation in society by documenting obstacles within or across national boundaries.<br/> <br/>The study has four phases that allow for multiple levels of comparison between audiences, geographies, and conditions such as exposure to other groups. We also allow for opinions to change, which stands in contrast to most of the cross-sectional survey work that dominates the field. In the first phase, each team will conduct an extensive document review to create historical-institutional profiles, including the scope of the nonprofit sector and relevant socio-cultural norms such as institutional trust toward the nonprofit, private, and public sectors. In the second phase, teams will convene both single-audience focus groups and mixed-audience Delphi groups to gather novel data. The third phase will produce practice-oriented reports, while the fourth phase involves academic book and article production and contains a conference event that serves not only to share knowledge, but serves as another round of data gathering to document opinion shift and further study the influence of peer learning on trust, accountability, and governance.<br/><br/>This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

08/20/2024

08/20/2024

None

Grant

47.075

1

4900

4900

2436973

{'FirstName': 'Elizabeth', 'LastName': 'Searing', 'PI_MID_INIT': None, 'PI_SUFX_NAME': None, 'PI_FULL_NAME': 'Elizabeth Searing', 'EmailAddress': 'Elizabeth.Searing@UTDallas.edu', 'NSF_ID': '000805101', 'StartDate': '08/20/2024', 'EndDate': None, 'RoleCode': 'Principal Investigator'}

{'Name': 'University of Texas at Dallas', 'CityName': 'RICHARDSON', 'ZipCode': '750803021', 'PhoneNumber': '9728832313', 'StreetAddress': '800 WEST CAMPBELL RD.', 'StreetAddress2': 'SP2.25', 'CountryName': 'United States', 'StateName': 'Texas', 'StateCode': 'TX', 'CONGRESSDISTRICT': '24', 'CONGRESS_DISTRICT_ORG': 'TX24', 'ORG_UEI_NUM': 'EJCVPNN1WFS5', 'ORG_LGL_BUS_NAME': 'UNIVERSITY OF TEXAS AT DALLAS', 'ORG_PRNT_UEI_NUM': None}

{'Name': 'University of Texas at Dallas', 'CityName': 'RICHARDSON', 'StateCode': 'TX', 'ZipCode': '750803021', 'StreetAddress': '800 WEST CAMPBELL RD.', 'CountryCode': 'US', 'CountryName': 'United States', 'StateName': 'Texas', 'CountryFlag': '1', 'CONGRESSDISTRICT': '24', 'CONGRESS_DISTRICT_PERF': 'TX24'}

{'Code': '188Y00', 'Text': 'T-AP-Trans-Atlantic Platform'}

2024~192213

{'url': 'https://www.nsf.gov/awardsearch/download?DownloadFileName=2024&All=true', 'xml': '2436973.xml'}

CDS&E: Data-driven modeling and analyses of CO2 transport in porous media

NSF

07/01/2024

03/31/2025

{'Value': 'Standard Grant'}

{'Code': '07020000', 'Directorate': {'Abbreviation': 'ENG', 'LongName': 'Directorate For Engineering'}, 'Division': {'Abbreviation': 'CBET', 'LongName': 'Div Of Chem, Bioeng, Env, & Transp Sys'}}

{'SignBlockName': 'Ron Joslin', 'PO_EMAI': 'rjoslin@nsf.gov', 'PO_PHON': '7032927030'}

The geological sequestration of CO2 is considered one of the most effective strategies to mitigate the adverse effects of climate change due to the CO2 emitted from significant stationary anthropogenic sources. However, successful implementation of large-scale CO2 sequestration involves operational uncertainties and risks due to the complex flow behavior of CO2 in subsurface conditions. This proposed research will develop computationally efficient data-driven models to accurately predict and analyze the complex flow behavior of CO2 flow in geological formations by analyzing an extensive set of data collected from experimental and numerical studies. This project connects the fundamental topics covered in core chemical engineering courses at University of Texas (UT) Tyler to hypothesis-driven research, which promotes maximum participation of students equipped with the tools needed to make meaningful contributions to this work. Furthermore, this project involves outreach activities to recruit and attract students from under-represented populations, in collaboration with UT Tyler University Academy and Tyler Junior College, to the critical areas of climate change and sustainable energy production. <br/><br/>The proposed research activities are organized into three focus areas, the results from which will have a significant influence on the modeling of the diffusive, reactive, and convective transport of CO2 in porous media saturated with brine and consisting of geological uncertainties: (1) Quantify and predict the dissolution and precipitation of minerals such as calcite at different temperatures, pressures, and initial cation concentrations due to the interaction with CO2 using experimental data; (2) Evaluate the effect of porosity and permeability on the flow behavior of CO2 using high-resolution images of core samples collected from public data portals; (3) Visualize, and quantify the effect of discrete fractures and uncertain reservoir properties on channeling and plume migration of CO2 in porous media using images and data from public repositories. The generated or collected data will be analyzed in each case using robust statistical models to achieve the project objectives, which will lead to potentially transformative technologies to improve long-term carbon storage in deep saline aquifers. Finally, this research project will increase public awareness and enhance scientific literacy around energy use and CO2 emissions for a diverse audience through public lectures, hands-on demonstrations, and other outreach programs.<br/><br/>This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

07/10/2024

07/10/2024

None

Grant

47.041

1

4900

4900

2436996

{'FirstName': 'Aaditya', 'LastName': 'Khanal', 'PI_MID_INIT': None, 'PI_SUFX_NAME': None, 'PI_FULL_NAME': 'Aaditya Khanal', 'EmailAddress': 'aaditya-khanal@utulsa.edu', 'NSF_ID': '000856116', 'StartDate': '07/10/2024', 'EndDate': None, 'RoleCode': 'Principal Investigator'}

{'Name': 'University of Tulsa', 'CityName': 'TULSA', 'ZipCode': '741049700', 'PhoneNumber': '9186312192', 'StreetAddress': '800 S TUCKER DR', 'StreetAddress2': None, 'CountryName': 'United States', 'StateName': 'Oklahoma', 'StateCode': 'OK', 'CONGRESSDISTRICT': '01', 'CONGRESS_DISTRICT_ORG': 'OK01', 'ORG_UEI_NUM': 'P23YK1EKPS51', 'ORG_LGL_BUS_NAME': 'UNIVERSITY OF TULSA', 'ORG_PRNT_UEI_NUM': None}

{'Name': 'University of Tulsa', 'CityName': 'TULSA', 'StateCode': 'OK', 'ZipCode': '741049700', 'StreetAddress': '800 S TUCKER DR', 'CountryCode': 'US', 'CountryName': 'United States', 'StateName': 'Oklahoma', 'CountryFlag': '1', 'CONGRESSDISTRICT': '01', 'CONGRESS_DISTRICT_PERF': 'OK01'}

[{'Code': '144300', 'Text': 'FD-Fluid Dynamics'}, {'Code': '808400', 'Text': 'CDS&E'}]

2023~157471

{'url': 'https://www.nsf.gov/awardsearch/download?DownloadFileName=2024&All=true', 'xml': '2436996.xml'}

Conference: Integrative science of kleptobiology and photosymbiosis: a workshop during a joint conference of three societies

NSF

08/01/2024

07/31/2025

{'Value': 'Standard Grant'}

{'Code': '08090000', 'Directorate': {'Abbreviation': 'BIO', 'LongName': 'Direct For Biological Sciences'}, 'Division': {'Abbreviation': 'IOS', 'LongName': 'Division Of Integrative Organismal Systems'}}

{'SignBlockName': 'Theodore Morgan', 'PO_EMAI': 'tmorgan@nsf.gov', 'PO_PHON': '7032927868'}

Harnessing the power of photosynthesis holds potential for new technologies ranging from clean and renewable solar energy, to biomedical advances in transplantation and wound healing. The investigators propose a day-long workshop that will bring over 30 leading experts, early-career scientists and students together to explore how organisms ranging from single cells to animals capture and use chloroplasts or other organelles from their food (“kleptobiology”) for their own benefit. They will compare these adaptations to traditional symbioses between animals and photosynthetic partners to gain new insights into how organisms from different branches of the Tree of Life work in an integrative manner, allowing the incredible efficiency of photosynthesis to temporarily power other organisms. By synthesizing recent advances in genomics, microscopy, field ecology, and biochemistry, they will gain new insights into the most promising directions for the field, and potential applications of biological insights from kleptobiology. Their workshop dedicated to kleptobiology will run in conjunction with a scientific conference taking place in August 2024. They will draw together a diverse group of leading researchers, early-career scientists and students who vary widely in their personal and academic backgrounds and training, which promotes interdisciplinary thinking and innovation. This working group will foster communication and collaboration, data sharing, and develop community resources to promote best practices and move quickly on new discoveries. Participants will also present their research and network during the three-day joint meeting of professional societies that follows. The 35 confirmed participants are diverse in career level, scientific interests, and personal identity/experience; half are students, postdocs or new faculty.<br/><br/>Integrative and genomic studies provide new opportunities to investigate how photosymbiotic relationships arise and promote holobiont success, with diverse applications from biophotovoltaics to tissue transplantation. This workshop will be the first to focus on kleptobiology, in which chemicals or chloroplasts are captured and integrated into the host cell. A one-day workshop will facilitate research coordination and synthesis for a nascent community, promoting the integration of new approaches, model systems, and emerging techniques. 35 participants are confirmed for the workshop during a joint meeting of three mollusc societies (AMS-WSM; Aug 4-7, 2024). Funds will offset travel, registration, and hotel costs for 18 U.S. attendees, with society funds covering international colleagues and other costs. Through presentations, break-out groups and panel discussions, the workshop will answer five questions: (1) What were the major advances in kleptobiology over the past 5 years; (2) What are the research priorities; (3) Where are the opportunities for breakthroughs, collaborations, and comparative studies; (4) How can efforts be better coordinated to share resources and methods; sustain communication; and synthesize and disseminate data; and (5) How can the researchers build a pipeline for interdisciplinary training? The goals are to produce new protocols, synthesize recent findings and challenges, and establish platforms for sharing information and resources to sustain this research network studying kleptobiology.<br/><br/>This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

07/25/2024

07/25/2024

None

Grant

47.074

1

4900

4900

2437002

{'FirstName': 'Patrick', 'LastName': 'Krug', 'PI_MID_INIT': 'J', 'PI_SUFX_NAME': None, 'PI_FULL_NAME': 'Patrick J Krug', 'EmailAddress': 'pkrug@calstatela.edu', 'NSF_ID': '000204630', 'StartDate': '07/25/2024', 'EndDate': None, 'RoleCode': 'Principal Investigator'}

{'Name': 'California State L A University Auxiliary Services Inc.', 'CityName': 'LOS ANGELES', 'ZipCode': '900324226', 'PhoneNumber': '3233433648', 'StreetAddress': '5151 STATE UNIVERSITY DR', 'StreetAddress2': None, 'CountryName': 'United States', 'StateName': 'California', 'StateCode': 'CA', 'CONGRESSDISTRICT': '34', 'CONGRESS_DISTRICT_ORG': 'CA34', 'ORG_UEI_NUM': 'C1ABLRAQTB48', 'ORG_LGL_BUS_NAME': 'CAL STATE LA UNIVERSITY AUXILIARY SERVICES INC', 'ORG_PRNT_UEI_NUM': None}

{'Name': 'California State L A University Auxiliary Services Inc.', 'CityName': 'LOS ANGELES', 'StateCode': 'CA', 'ZipCode': '900324226', 'StreetAddress': '5151 STATE UNIVERSITY DR', 'CountryCode': 'US', 'CountryName': 'United States', 'StateName': 'California', 'CountryFlag': '1', 'CONGRESSDISTRICT': '34', 'CONGRESS_DISTRICT_PERF': 'CA34'}

{'Code': '765700', 'Text': 'Integrtv Ecological Physiology'}

2024~14996

{'url': 'https://www.nsf.gov/awardsearch/download?DownloadFileName=2024&All=true', 'xml': '2437002.xml'}

Conference: SAIL 2024: Summit for AI Institutes Leadership and Federated Activities; Pittsburgh, Pennsylvania; 6-9 October 2024

NSF

08/15/2024

07/31/2025

{'Value': 'Standard Grant'}

{'Code': '05020000', 'Directorate': {'Abbreviation': 'CSE', 'LongName': 'Direct For Computer & Info Scie & Enginr'}, 'Division': {'Abbreviation': 'IIS', 'LongName': 'Div Of Information & Intelligent Systems'}}

{'SignBlockName': 'James Donlon', 'PO_EMAI': 'jdonlon@nsf.gov', 'PO_PHON': '7032928074'}

This conference is the third annual program-wide meeting in the National AI Research Institutes Program. The Summit for AI Institutes Leadership (SAIL 2024) is an NSF-sponsored conference organized and executed by the program’s hub activity, the AI Institutes Virtual Organization (AIVO). The conference gathers the leaders and other key personnel from all AI Institutes to foster community building of those Institutes and other related activities into a network of collaborating organizations conducting knowledge exchange, growing their own competencies, and engaging with the broader public. The conference will take place 6-9 October 2024 in Pittsburgh, Pennsylvania.<br/><br/>This conference aims to maximize the value of the AI Institutes as a flagship national AI investment. The conference delivers on the intent of NSF and its funding partners to continue to nurture the AI Institutes into a fully cohered national program, resulting in synergy across the constituent institutes that is greater than the sum of its parts. This gathering builds upon the successes and lessons from the previous SAIL events (2022 and 2023) and continues a successful record of establishing SAIL as the flagship event for the National AI Research Institutes program. The conference program addresses the needs of AI Institutes in various stages of their lifecycle, from those in their fourth year to newly-established AI Institutes. This greatly enhances knowledge transfer among all. The program includes knowledge exchange about education and outreach, project management, computing and research infrastructure, communications, workforce development, and ethics. The conference is comprised of a balance of community-moderated panels with plenary sessions and other program-wide community building. A workshop day prior to the main conference allows the program’s special interest groups to hold smaller community workshops around topics of interest withing a specialized area, and across institute boundaries. Following the SAIL conference events, an AI Institutes Expo Day co-located in Pittsburgh will build upon this gathering to create a program-organized public engagement event. Expo day will combine Institute exhibits, talks, panels, and networking venues to allow the public to directly and efficiently engage with the AI Institutes toward greater understanding of AI and the potential initiation of new collaborations.<br/><br/>This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

08/13/2024

08/13/2024

None

Grant

47.041, 47.049, 47.050, 47.070, 47.076

1

4900

4900

2437003

[{'FirstName': 'Ilias', 'LastName': 'Tagkopoulos', 'PI_MID_INIT': None, 'PI_SUFX_NAME': None, 'PI_FULL_NAME': 'Ilias Tagkopoulos', 'EmailAddress': 'iliast@cs.ucdavis.edu', 'NSF_ID': '000531392', 'StartDate': '08/13/2024', 'EndDate': None, 'RoleCode': 'Co-Principal Investigator'}, {'FirstName': 'Stephen', 'LastName': 'Brown', 'PI_MID_INIT': 'F', 'PI_SUFX_NAME': None, 'PI_FULL_NAME': 'Stephen F Brown', 'EmailAddress': 'sfbrown@ucdavis.edu', 'NSF_ID': '000880898', 'StartDate': '08/13/2024', 'EndDate': None, 'RoleCode': 'Principal Investigator'}]

{'Name': 'University of California-Davis', 'CityName': 'DAVIS', 'ZipCode': '956186153', 'PhoneNumber': '5307547700', 'StreetAddress': '1850 RESEARCH PARK DR STE 300', 'StreetAddress2': None, 'CountryName': 'United States', 'StateName': 'California', 'StateCode': 'CA', 'CONGRESSDISTRICT': '04', 'CONGRESS_DISTRICT_ORG': 'CA04', 'ORG_UEI_NUM': 'TX2DAGQPENZ5', 'ORG_LGL_BUS_NAME': 'UNIVERSITY OF CALIFORNIA, DAVIS', 'ORG_PRNT_UEI_NUM': None}

{'Name': 'University of California-Davis', 'CityName': 'DAVIS', 'StateCode': 'CA', 'ZipCode': '956186153', 'StreetAddress': '1850 RESEARCH PARK DR STE 300', 'CountryCode': 'US', 'CountryName': 'United States', 'StateName': 'California', 'CountryFlag': '1', 'CONGRESSDISTRICT': '04', 'CONGRESS_DISTRICT_PERF': 'CA04'}

[{'Code': '006Y00', 'Text': 'OE Operations Engineering'}, {'Code': '132Y00', 'Text': 'AI Research Institutes'}, {'Code': '140700', 'Text': 'CFS-Combustion & Fire Systems'}, {'Code': '725900', 'Text': 'AISL'}, {'Code': '755300', 'Text': 'PHYSICS AT THE INFO FRONTIER'}]

2024~203270

{'url': 'https://www.nsf.gov/awardsearch/download?DownloadFileName=2024&All=true', 'xml': '2437003.xml'}

Impact of Displacement on the Development of Social Preferences and Trust

NSF

09/15/2024

08/31/2027

{'Value': 'Standard Grant'}

{'Code': '04010000', 'Directorate': {'Abbreviation': 'SBE', 'LongName': 'Direct For Social, Behav & Economic Scie'}, 'Division': {'Abbreviation': 'SMA', 'LongName': 'SBE Off Of Multidisciplinary Activities'}}

{'SignBlockName': 'Antwan Jones', 'PO_EMAI': 'aajones@nsf.gov', 'PO_PHON': '7032924973'}

The world is increasingly facing displacements due to conflicts and climate change. These displacements, especially those caused by violence, profoundly affect children's cognitive and emotional development, with potential long-term societal repercussions. This project examines how displacement impacts the development of social preferences and trust in children. By studying the influence of these displacements on trust, cooperation, and social norms, this research advances social sciences and informs policies promoting prosocial behavior and trust. The findings aim to guide community initiatives that support displaced families, fostering social cohesion and economic integration. Ultimately, the goal is to build more cohesive and resilient communities, thereby enhancing societal welfare, national stability, prosperity, and overall societal benefits. <br/><br/>The project studies how displacement affects children's and adolescents’ social preferences, trust, and social norms, particularly focusing on those displaced by intergroup conflicts. Through behavioral economics experiments and a randomized controlled trial (RCT) involving children, adolescents, and their parents, the project will evaluate fairness, trust, trustworthiness, cooperation, and gender-specific social norms. Including adults in the study aims to understand how social preferences and norms are transmitted across generations. This multidisciplinary project integrates expertise in education, behavioral economics, and developmental psychology. The research seeks to provide evidence-based recommendations for policy interventions that enhance prosocial motivations, trust, and cooperation, ultimately supporting governance and societal well-being.<br/><br/>This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

08/19/2024

08/19/2024

None

Grant

47.075

1

4900

4900

2437004

{'FirstName': 'Jean', 'LastName': 'Decety', 'PI_MID_INIT': None, 'PI_SUFX_NAME': None, 'PI_FULL_NAME': 'Jean Decety', 'EmailAddress': 'decety@uchicago.edu', 'NSF_ID': '000427275', 'StartDate': '08/19/2024', 'EndDate': None, 'RoleCode': 'Principal Investigator'}

{'Name': 'University of Chicago', 'CityName': 'CHICAGO', 'ZipCode': '606375418', 'PhoneNumber': '7737028669', 'StreetAddress': '5801 S ELLIS AVE', 'StreetAddress2': None, 'CountryName': 'United States', 'StateName': 'Illinois', 'StateCode': 'IL', 'CONGRESSDISTRICT': '01', 'CONGRESS_DISTRICT_ORG': 'IL01', 'ORG_UEI_NUM': 'ZUE9HKT2CLC9', 'ORG_LGL_BUS_NAME': 'UNIVERSITY OF CHICAGO', 'ORG_PRNT_UEI_NUM': 'ZUE9HKT2CLC9'}

{'Name': 'University of Chicago', 'CityName': 'CHICAGO', 'StateCode': 'IL', 'ZipCode': '606375418', 'StreetAddress': '5801 S ELLIS AVE', 'CountryCode': 'US', 'CountryName': 'United States', 'StateName': 'Illinois', 'CountryFlag': '1', 'CONGRESSDISTRICT': '01', 'CONGRESS_DISTRICT_PERF': 'IL01'}

{'Code': '188Y00', 'Text': 'T-AP-Trans-Atlantic Platform'}

2024~191980

{'url': 'https://www.nsf.gov/awardsearch/download?DownloadFileName=2024&All=true', 'xml': '2437004.xml'}

The Role of Inclusive Metascience Observatories in Science Diplomacy

NSF

09/15/2024

08/31/2026

{'Value': 'Standard Grant'}

{'Code': '04010000', 'Directorate': {'Abbreviation': 'SBE', 'LongName': 'Direct For Social, Behav & Economic Scie'}, 'Division': {'Abbreviation': 'SMA', 'LongName': 'SBE Off Of Multidisciplinary Activities'}}

{'SignBlockName': 'Antwan Jones', 'PO_EMAI': 'aajones@nsf.gov', 'PO_PHON': '7032924973'}

The key aim of this project is to examine the role of evidence-informed science diplomacy as a strategic instrument to address opportunities for strengthening and threats to maintaining democracy, governance, and trust (DGT). This work contributes towards a conceptualization of a concept with rising importance on the international stage and presents an innovative approach to integrating metascientific evidence into the practice of diplomacy. The intended impact of the project is to strengthen DGT by supporting data gathering and production of inclusive indicators and amplifying trust in science within and across nations. <br/><br/>This research will contribute to our theoretical, historical, and empirical understanding of science diplomacy and advance knowledge across several fields (e.g., science and technology policy, science communication, political science, and international relations). This project will utilize qualitative and quantitative methods to (1) understand the relationship between science diplomacy and DGT; (2) conceptualize and operationalize metascience observatories and investigate the extent to which they can be leveraged to improve science diplomacy; and (3) explore how threats to DGT could be mitigated and opportunities seized through inclusive metascience observatories. The outputs will include both academic-oriented products, as well as communications to policymakers and the wider public. In addition to these products, outcomes will include communities of practice and training opportunities. The long-term goal of the project is to strengthen DGT through evidenced-informed science diplomacy.<br/><br/>This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

08/19/2024

08/19/2024

None

Grant

47.075

1

4900

4900

2437013

[{'FirstName': 'Nicholas', 'LastName': 'Vonortas', 'PI_MID_INIT': 'S', 'PI_SUFX_NAME': None, 'PI_FULL_NAME': 'Nicholas S Vonortas', 'EmailAddress': 'vonortas@gwu.edu', 'NSF_ID': '000160910', 'StartDate': '08/19/2024', 'EndDate': None, 'RoleCode': 'Co-Principal Investigator'}, {'FirstName': 'Cassidy', 'LastName': 'Sugimoto', 'PI_MID_INIT': 'R', 'PI_SUFX_NAME': None, 'PI_FULL_NAME': 'Cassidy R Sugimoto', 'EmailAddress': 'sugimoto@gatech.edu', 'NSF_ID': '000569061', 'StartDate': '08/19/2024', 'EndDate': None, 'RoleCode': 'Principal Investigator'}, {'FirstName': 'Juan', 'LastName': 'Rogers', 'PI_MID_INIT': 'D', 'PI_SUFX_NAME': None, 'PI_FULL_NAME': 'Juan D Rogers', 'EmailAddress': 'jdrogers@gatech.edu', 'NSF_ID': '000290825', 'StartDate': '08/19/2024', 'EndDate': None, 'RoleCode': 'Co-Principal Investigator'}, {'FirstName': 'Philip', 'LastName': 'Shapira', 'PI_MID_INIT': None, 'PI_SUFX_NAME': None, 'PI_FULL_NAME': 'Philip Shapira', 'EmailAddress': 'ps25@prism.gatech.edu', 'NSF_ID': '000099288', 'StartDate': '08/19/2024', 'EndDate': None, 'RoleCode': 'Co-Principal Investigator'}]

{'Name': 'Georgia Tech Research Corporation', 'CityName': 'ATLANTA', 'ZipCode': '303186395', 'PhoneNumber': '4048944819', 'StreetAddress': '926 DALNEY ST NW', 'StreetAddress2': None, 'CountryName': 'United States', 'StateName': 'Georgia', 'StateCode': 'GA', 'CONGRESSDISTRICT': '05', 'CONGRESS_DISTRICT_ORG': 'GA05', 'ORG_UEI_NUM': 'EMW9FC8J3HN4', 'ORG_LGL_BUS_NAME': 'GEORGIA TECH RESEARCH CORP', 'ORG_PRNT_UEI_NUM': 'EMW9FC8J3HN4'}

{'Name': 'Georgia Tech Research Corporation', 'CityName': 'ATLANTA', 'StateCode': 'GA', 'ZipCode': '303186395', 'StreetAddress': '926 DALNEY ST NW', 'CountryCode': 'US', 'CountryName': 'United States', 'StateName': 'Georgia', 'CountryFlag': '1', 'CONGRESSDISTRICT': '05', 'CONGRESS_DISTRICT_PERF': 'GA05'}

{'Code': '188Y00', 'Text': 'T-AP-Trans-Atlantic Platform'}

2024~199984

{'url': 'https://www.nsf.gov/awardsearch/download?DownloadFileName=2024&All=true', 'xml': '2437013.xml'}

Conference: 2025 Mitochondria in Health and Disease: Towards a Comprehensive View of Mitochondrial Biology

NSF

07/15/2024

06/30/2025

{'Value': 'Standard Grant'}

{'Code': '08070000', 'Directorate': {'Abbreviation': 'BIO', 'LongName': 'Direct For Biological Sciences'}, 'Division': {'Abbreviation': 'MCB', 'LongName': 'Div Of Molecular and Cellular Bioscience'}}

{'SignBlockName': 'Manju Hingorani', 'PO_EMAI': 'mhingora@nsf.gov', 'PO_PHON': '7032927323'}

The award will provide support for graduate students and post-doctoral researchers to attend the 2025 Gordon Research Seminar (GRS) Towards a Comprehensive View of Mitochondrial Biology (March 22-23, 2024, in Ventura, CA). The GRS will precede the associated Gordon Research Conference (GRC) on Mitochondria Metabolism and Signaling. These meetings bring together both young and established scientists from various disciplines to explore a broad range of questions related to mitochondrial function, from mechanisms and functions to the development of innovative research tools. The GRS is a unique platform that enables early career scientists to discuss the most up-to-date research through seminars and poster sessions, exchange ideas, technologies, and resources informally, and expand their professional networks. This conference is crucial for the training and professional development of the next generation of researchers and to broaden participation in the field of mitochondrial biology. The GRS program will feature a panel discussion on career choices with accomplished scientists from diverse professional backgrounds. The event also offers career development experience for the co-chairs and organizers, who are PhD students and postdocs.<br/><br/>Recent advances in mitochondrial biology, particularly the molecular targets of mitochondrial uncouplers and protonophores, will be the keynote talk of the GRS. Through a synergy of fundamental mitochondrial biology techniques such as patch-clamp methodologies, in silico molecular docking, and high-throughput CRISPR screens coupled with live cell imaging, participants will explore groundbreaking discoveries. The transformative impact of multi-omics strategies and novel mitochondrial genome-editing tools, such as mitoARCUS and mitoDdCBE, will be highlighted, revolutionizing our understanding of mitochondrial genetics and metabolism. Additionally, the expanding spectrum of biological processes governed by mitochondria, ranging from cellular metabolism and epigenetics to immune regulation and signaling, will be discussed, emphasizing the diverse effects of mitochondrial dysfunction on cell fate.<br/><br/>This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

07/08/2024

07/08/2024

None

Grant

47.074

1

4900

4900

2437022

{'FirstName': 'Mateus', 'LastName': 'Prates Mori', 'PI_MID_INIT': None, 'PI_SUFX_NAME': None, 'PI_FULL_NAME': 'Mateus Prates Mori', 'EmailAddress': 'mateus.mori@nih.gov', 'NSF_ID': '0000A07FR', 'StartDate': '07/08/2024', 'EndDate': None, 'RoleCode': 'Principal Investigator'}

{'Name': 'Gordon Research Conferences', 'CityName': 'EAST GREENWICH', 'ZipCode': '028183454', 'PhoneNumber': '4017834011', 'StreetAddress': '5586 POST RD UNIT 2', 'StreetAddress2': None, 'CountryName': 'United States', 'StateName': 'Rhode Island', 'StateCode': 'RI', 'CONGRESSDISTRICT': '02', 'CONGRESS_DISTRICT_ORG': 'RI02', 'ORG_UEI_NUM': 'XL5ANMKWN557', 'ORG_LGL_BUS_NAME': 'GORDON RESEARCH CONFERENCES', 'ORG_PRNT_UEI_NUM': None}

{'Name': 'Gordon Research Conferences', 'CityName': 'EAST GREENWICH', 'StateCode': 'RI', 'ZipCode': '028183454', 'StreetAddress': '5586 POST RD UNIT 2', 'CountryCode': 'US', 'CountryName': 'United States', 'StateName': 'Rhode Island', 'CountryFlag': '1', 'CONGRESSDISTRICT': '02', 'CONGRESS_DISTRICT_PERF': 'RI02'}

[{'Code': '111200', 'Text': 'Genetic Mechanisms'}, {'Code': '111400', 'Text': 'Cellular Dynamics and Function'}]

2024~12000

{'url': 'https://www.nsf.gov/awardsearch/download?DownloadFileName=2024&All=true', 'xml': '2437022.xml'}

EAGER: Entangled Light Generation via the Dynamical Casimir Effect

NSF

08/01/2024

07/31/2025

{'Value': 'Standard Grant'}

{'Code': '07010000', 'Directorate': {'Abbreviation': 'ENG', 'LongName': 'Directorate For Engineering'}, 'Division': {'Abbreviation': 'ECCS', 'LongName': 'Div Of Electrical, Commun & Cyber Sys'}}

{'SignBlockName': 'Margaret Kim', 'PO_EMAI': 'sekim@nsf.gov', 'PO_PHON': '7032922967'}

Nontechnical Description:<br/>Next-generation technologies using quantum entangled light promise a completely secure and unbreakable method of communications. Development of this new technology is critical for security in a wide range of communications including transactions conducted over the internet for national security and military communications. Current methods for generating entangled light needed for these applications, however, rely on complicated nonlinear optical generation methods and exotic materials that will be challenging to integrate into current microelectronics. Our proposed work will develop a novel method for generating quantum entangled light using materials commonly employed in microelectronics. We will demonstrate the modulation of an optical cavity using integrated phase change materials that will be needed to generate quantum-entangled light.<br/> <br/>To recruit the next generation of researchers into quantum information science and technology in both Middle Tennessee and Central Texas, the PIs will develop new outreach actives in both Nashville and Waco that will expose area high school students to the growing field of quantum information science and engineering through a new summer outreach program in Waco and expanded outreach programs in the Vanderbilt Summer Science Academy and the development of a new minor at Vanderbilt in quantum information science and engineering.<br/><br/>Technical Description:<br/>We propose to construct and study devices in which entangled light can be generated by femtosecond excitation of a phase-change material. We will build layered structures in which the phase-change material is deposited on a transparent oxide and study the optical transmission of the induced diffraction grating. We will test these devices to demonstrate the operation of the diffraction grating at near normally incident light and demonstrate modulation of the cavity in the oxide layer.<br/> <br/>The intellectual merit of this proposal is rooted in its ambition to realize in practice the intuitively appealing moving-mirror concept of the dynamical Casimir effect (DCE), using phase-change materials to provide wavelength selectivity in extracting photons from the quantum vacuum at wavelengths compatible with silicon photonics technology. This project exists intellectually at the boundaries between quantum field theory, ultrafast optical physics, and the materials science of quantum (phase-change) materials. Students engaged in this project will be trained in this emerging technological field. Success in this project will yield a novel route to creating entangled photon pairs in a way that is intrinsically compatible with silicon photonics without the need for the high-power lasers required, for example, for pair creation by parametric down conversion in nonlinear crystals. The Broader Impacts of this work will include novel outreach activities to engage the next generation of students with the emerging field of quantum information science and engineering (QISE) and encourage them to consider careers in STEM. Drawing upon the diverse populations in Central Texas and Middle Tennessee, we can use these programs to further the National Science Foundation goal of broadening and diversifying the future workforce<br/><br/>This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

07/31/2024

07/31/2024

None

Grant

47.041

1

4900

4900

2437031

[{'FirstName': 'Richard', 'LastName': 'Haglund', 'PI_MID_INIT': 'F', 'PI_SUFX_NAME': 'Jr', 'PI_FULL_NAME': 'Richard F Haglund', 'EmailAddress': 'richard.haglund@vanderbilt.edu', 'NSF_ID': '000389931', 'StartDate': '07/31/2024', 'EndDate': None, 'RoleCode': 'Co-Principal Investigator'}, {'FirstName': 'David', 'LastName': 'Hilton', 'PI_MID_INIT': 'J', 'PI_SUFX_NAME': None, 'PI_FULL_NAME': 'David J Hilton', 'EmailAddress': 'david_hilton@baylor.edu', 'NSF_ID': '000516590', 'StartDate': '07/31/2024', 'EndDate': None, 'RoleCode': 'Principal Investigator'}]

{'Name': 'Baylor University', 'CityName': 'WACO', 'ZipCode': '767061003', 'PhoneNumber': '2547103817', 'StreetAddress': '700 S UNIVERSITY PARKS DR', 'StreetAddress2': None, 'CountryName': 'United States', 'StateName': 'Texas', 'StateCode': 'TX', 'CONGRESSDISTRICT': '17', 'CONGRESS_DISTRICT_ORG': 'TX17', 'ORG_UEI_NUM': 'C6T9BYG5EYX5', 'ORG_LGL_BUS_NAME': 'BAYLOR UNIVERSITY', 'ORG_PRNT_UEI_NUM': None}

{'Name': 'Baylor University', 'CityName': 'WACO', 'StateCode': 'TX', 'ZipCode': '767061003', 'StreetAddress': '700 S UNIVERSITY PARKS DR', 'CountryCode': 'US', 'CountryName': 'United States', 'StateName': 'Texas', 'CountryFlag': '1', 'CONGRESSDISTRICT': '17', 'CONGRESS_DISTRICT_PERF': 'TX17'}

{'Code': '151700', 'Text': 'EPMD-ElectrnPhoton&MagnDevices'}

2024~150000

{'url': 'https://www.nsf.gov/awardsearch/download?DownloadFileName=2024&All=true', 'xml': '2437031.xml'}

Conference: Spread the Sizzle: Expanding the Undergraduate Spectrum Workshop

NSF

09/01/2024

08/31/2025

{'Value': 'Standard Grant'}

{'Code': '03020000', 'Directorate': {'Abbreviation': 'MPS', 'LongName': 'Direct For Mathematical & Physical Scien'}, 'Division': {'Abbreviation': 'AST', 'LongName': 'Division Of Astronomical Sciences'}}

{'SignBlockName': 'John Chapin', 'PO_EMAI': 'jchapin@nsf.gov', 'PO_PHON': '7032928222'}

This project is a collaboration between Baylor University, Virginia Tech, New York Institute of Technology, and Colorado State University to conduct undergraduate workshops on electromagnetic spectrum science and engineering in summer 2025 at each of the four campuses. With great challenges created by wireless spectrum congestion and the ongoing emergence of new technologies, policies, and spectrum sharing approaches, a nationwide effort is needed to develop the future spectrum workforce. This need was emphasized by the 2023 National Spectrum Strategy, of which one pillar is Expanded Spectrum Expertise and Elevated National Awareness. This project supports workforce development, providing funds for a four-day residential workshop, the “Spectrum Sizzle,” on each campus. In the Spectrum Sizzle, undergraduate students participate in hands-on activities related to spectrum policy, communication systems, radar systems, passive systems, and circuits. Additionally, there are panel sessions discussing spectrum careers and graduate school, as well as fun events (such as a campus transmitter hunt). Participating students are recruited from the region surrounding each host location, not just the host campus. The first two Spectrum Sizzle events were held at Baylor University in summer 2023 and summer 2024, beginning with 15 students in 2023 and expanding to 40 students with a waiting list of over 100 in 2024. Coordinated through the Baylor-led SMART Hub organization (Hub for Spectrum Management with Adaptive and Reconfigurable Technology), workshops will be held at four campuses in summer 2025, spread around the country so more students can participate. Organizers intentionally recruit students from Minority Serving Institutions, and a significant goal of implementing these Sizzle events is to develop workforce developers who will multiply spectrum outreach to undergraduate students and others across the country.<br/> <br/>Each Spectrum Sizzle Undergraduate Spectrum Workshop will be scheduled over three and a half days during Summer 2025 and will host approximately 30-40 students (150-160 total across four workshops supported by the award). Planned lecture and laboratory topics include a Mock FCC Proceeding in which participants argue and then adjudicate a spectrum case involving interference; a module on Communications Systems Coexistence in which participants use spectrum analyzers and learn about modulation techniques; a module on Radar System Testing including operating and understanding a homemade radar system; a module on Passive Systems/Spectrum Analysis including lectures on scientific systems and laboratory work to measure different frequency bands; and a module on Filter Circuits including design and measurement of a filter that meets a specification.<br/><br/>This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

08/29/2024

08/29/2024

None

Grant

47.049

1

4900

4900

2437038

[{'FirstName': 'Steven', 'LastName': 'Reising', 'PI_MID_INIT': 'C', 'PI_SUFX_NAME': None, 'PI_FULL_NAME': 'Steven C Reising', 'EmailAddress': 'Steven.Reising@ColoState.edu', 'NSF_ID': '000291822', 'StartDate': '08/29/2024', 'EndDate': None, 'RoleCode': 'Co-Principal Investigator'}, {'FirstName': 'Carl', 'LastName': 'Dietrich', 'PI_MID_INIT': 'B', 'PI_SUFX_NAME': 'Jr', 'PI_FULL_NAME': 'Carl B Dietrich', 'EmailAddress': 'cdietric@vt.edu', 'NSF_ID': '000246069', 'StartDate': '08/29/2024', 'EndDate': None, 'RoleCode': 'Co-Principal Investigator'}, {'FirstName': 'Charles', 'LastName': 'Baylis', 'PI_MID_INIT': 'P', 'PI_SUFX_NAME': None, 'PI_FULL_NAME': 'Charles P Baylis', 'EmailAddress': 'Charles_Baylis@baylor.edu', 'NSF_ID': '000371791', 'StartDate': '08/29/2024', 'EndDate': None, 'RoleCode': 'Principal Investigator'}, {'FirstName': 'Batu', 'LastName': 'Chalise', 'PI_MID_INIT': None, 'PI_SUFX_NAME': None, 'PI_FULL_NAME': 'Batu Chalise', 'EmailAddress': 'bchalise@nyit.edu', 'NSF_ID': '000735561', 'StartDate': '08/29/2024', 'EndDate': None, 'RoleCode': 'Co-Principal Investigator'}]

{'Name': 'Baylor University', 'CityName': 'WACO', 'ZipCode': '767061003', 'PhoneNumber': '2547103817', 'StreetAddress': '700 S UNIVERSITY PARKS DR', 'StreetAddress2': None, 'CountryName': 'United States', 'StateName': 'Texas', 'StateCode': 'TX', 'CONGRESSDISTRICT': '17', 'CONGRESS_DISTRICT_ORG': 'TX17', 'ORG_UEI_NUM': 'C6T9BYG5EYX5', 'ORG_LGL_BUS_NAME': 'BAYLOR UNIVERSITY', 'ORG_PRNT_UEI_NUM': None}

{'Name': 'Baylor University', 'CityName': 'WACO', 'StateCode': 'TX', 'ZipCode': '767061003', 'StreetAddress': '700 S UNIVERSITY PARKS DR', 'CountryCode': 'US', 'CountryName': 'United States', 'StateName': 'Texas', 'CountryFlag': '1', 'CONGRESSDISTRICT': '17', 'CONGRESS_DISTRICT_PERF': 'TX17'}

{'Code': '151Y00', 'Text': 'SII-Spectrum Innovation Initia'}

2024~345711

{'url': 'https://www.nsf.gov/awardsearch/download?DownloadFileName=2024&All=true', 'xml': '2437038.xml'}

RAPID: Reprocessing the Defense Meteorological Satellite Program Special Sensor Ions, Electrons, Scintillation (DMSP SSIES-3) Ionospheric Data to Level-2 Quality

NSF

08/01/2024

07/31/2025

{'Value': 'Standard Grant'}

{'Code': '06020200', 'Directorate': {'Abbreviation': 'GEO', 'LongName': 'Directorate For Geosciences'}, 'Division': {'Abbreviation': 'AGS', 'LongName': 'Div Atmospheric & Geospace Sciences'}}

{'SignBlockName': 'Tai-Yin Huang', 'PO_EMAI': 'thuang@nsf.gov', 'PO_PHON': '7032924943'}

This RAPID award aims to continue to process, curate, and distribute plasma data from the operational DMSP spacecraft for the space science community during this critical period of 2024-2025 as we reach solar maximum. We are currently nearing the maximum of the 11-year solar cycle (with the peak expected sometime in 2025) as shown by the recent increase in the number and intensity of geomagnetic storms and activity. An example of this is the “Mother’s Day Storm” in May 2024 which was the largest geomagnetic storm to hit the Earth since 2003 resulting in auroras that were visible in most of the continental United States. These storms influence much of our civilian and military technological and infrastructure such as satellite communications, GPS, and power grid operations. The publicly available thermal plasma observations (densities, temperatures, flows, and composition from the SSIES-3 package) from the operational DMSP spacecraft were of only level 1 quality and contained large amounts of poor-quality data. Curating this database of the SSIES-3 ionospheric observations and providing it to the space science community will allow the full community to use these data in their own modeling and research efforts. These ionospheric data are necessary as inputs for numerous researchers and groups using them to develop a better understanding of the behavior and dynamics of the ionosphere. The curated data will be delivered to the U.S. NSF-funded Madrigal and NASA SPDF data centers for public distribution and use.<br/><br/>Prior to the team’s previous work, the publicly available thermal plasma observations (ion densities, temperatures, flows, and composition from the SSIES-3 package) from the operational DMSP spacecraft (F16, F17, and F18) were of only level-1 quality and contained large amounts of poor-quality data that were not flagged as such for the end user. During the past four years, the team updated the data reduction code and reprocessed over 25 satellite-years of SSIES-3 data from 2003 onward producing a level-2 quality dataset along with quality flags on most of the parameters. These improved level-2 data were delivered to the U.S. NSF-funded Madrigal and NASA SPDF data centers for public distribution and use. This RAPID award will fill the gap in the availability of these data products during the solar maximum just when they are most critically needed by the space science research community. The team will continue to produce these data for all three spacecraft for all of 2024 and most of the 2025 period. These level-2 data will be delivered to the data centers for the space science community’s use. As time permits, the team will work backwards from 2022 to fill in some (but not all) of the gap in the data that still needs to be reprocessed.<br/><br/>This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

08/05/2024

08/05/2024

None

Grant

47.050

1

4900

4900

2437055

{'FirstName': 'Marc', 'LastName': 'Hairston', 'PI_MID_INIT': 'R', 'PI_SUFX_NAME': None, 'PI_FULL_NAME': 'Marc R Hairston', 'EmailAddress': 'hairston@utdallas.edu', 'NSF_ID': '000367844', 'StartDate': '08/05/2024', 'EndDate': None, 'RoleCode': 'Principal Investigator'}

{'Name': 'University of Texas at Dallas', 'CityName': 'RICHARDSON', 'ZipCode': '750803021', 'PhoneNumber': '9728832313', 'StreetAddress': '800 WEST CAMPBELL RD.', 'StreetAddress2': 'SP2.25', 'CountryName': 'United States', 'StateName': 'Texas', 'StateCode': 'TX', 'CONGRESSDISTRICT': '24', 'CONGRESS_DISTRICT_ORG': 'TX24', 'ORG_UEI_NUM': 'EJCVPNN1WFS5', 'ORG_LGL_BUS_NAME': 'UNIVERSITY OF TEXAS AT DALLAS', 'ORG_PRNT_UEI_NUM': None}

{'Name': 'University of Texas at Dallas', 'CityName': 'RICHARDSON', 'StateCode': 'TX', 'ZipCode': '750803021', 'StreetAddress': '800 WEST CAMPBELL RD.', 'CountryCode': 'US', 'CountryName': 'United States', 'StateName': 'Texas', 'CountryFlag': '1', 'CONGRESSDISTRICT': '24', 'CONGRESS_DISTRICT_PERF': 'TX24'}

{'Code': '152100', 'Text': 'AERONOMY'}

2024~99290

{'url': 'https://www.nsf.gov/awardsearch/download?DownloadFileName=2024&All=true', 'xml': '2437055.xml'}

Collaborative Research: Causal Structures: Experiments and Machine Learning

NSF

07/01/2024

08/31/2026

{'Value': 'Standard Grant'}

{'Code': '04050000', 'Directorate': {'Abbreviation': 'SBE', 'LongName': 'Direct For Social, Behav & Economic Scie'}, 'Division': {'Abbreviation': 'SES', 'LongName': 'Divn Of Social and Economic Sciences'}}

{'SignBlockName': 'Nancy Lutz', 'PO_EMAI': 'nlutz@nsf.gov', 'PO_PHON': '7032927280'}

To make decisions, people must rely on their understanding of the relevant environment: what are the causes and outcomes of the various forces at play. In other words, in many settings, including economic ones, people rely on subjective causal models (or narratives) to understand the world. Such models help agents organize and interpret information, allowing them to make forecasts about the future, and providing them with a way to evaluate counterfactuals. The main goal of this research is to take a first step towards understanding how economic agents come to adopt (possibly incorrect) models and how this depends on the information available to them. The researchers will approach this topic from two different perspectives. The first involves a series of experiments that aim to understand how people’s subjective models arise from patterns they identify in data. Some experiments will be conducted in an abstract setting, while others involve natural context. Natural context can trigger preconceptions about how different variables are associated with each other that may help or hinder people from correctly identifying actual patterns in a set of observations. The second approach aims to better understand whether news media plays a role in heterogeneous subjective models. The goal is to study whether different news outlets organize and explain the same outcomes using different causal models.<br/><br/>A growing literature in economic theory studies ramifications of adopting possibly incorrect subjective models, referring to economic agents relying on such models as ‘misspecified.’ But, for the most part, the literature is silent on how a person comes to adopt a subjective model to begin with, how such a subjective model may depend on the setting, and how it may be shaped by the person’s experiences. In addition, it is an open question under what conditions people adopt subjective models that are consistent with the true data generating process. The goal of this research is to take a first step towards understanding how such misspecifications may arise and how they depend on features of the data-generating process. The researchers will approach the topic from two different perspectives. A first approach involves a series of laboratory experiments to understand how people extract patterns from their observations. The novel experimental design asks subjects to organize different sets of observations (data) with the goal of making predictions in similar situations. The experimental data will let the researchers understand whether the predictions subjects make in each environment are consistent with them using some model that posits specific statistical relationships between different variables. Complemented with ancillary non-choice data that emerges as a by-product of the experimental design, the results will provide insights into how people form models of the world by studying data and how they use these models to make predictions. Experiments will be conducted both with an abstract setting and with context. Understanding how people come to adopt (possibly incorrect) models and how this is impacted by the information available to them is important to determine in what situations they are more vulnerable to being manipulated. Furthermore, it can help us design policies that are effective in correcting beliefs and inducing optimal behavior. The second approach aims to better understand whether news media plays a role in shaping heterogeneous subjective models. The goal is to study whether different news outlets organize and explain the same outcomes using different causal models. To do so, the researchers will use an end-to-end trained Machine Learning pipeline that will take text (news articles) as input and identify the main causal statements advanced in this text as output. Documenting the heterogeneous causal models propagated by news outlets is important for understanding why voters with different political affiliation disagree on the optimal response to problems that are accepted by both sides.<br/><br/>This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

07/01/2024

07/01/2024

None

Grant

47.075

1

4900

4900

2437062

{'FirstName': 'Sevgi', 'LastName': 'Yuksel', 'PI_MID_INIT': None, 'PI_SUFX_NAME': None, 'PI_FULL_NAME': 'Sevgi Yuksel', 'EmailAddress': 'sevgi.yuksel@ucsb.edu', 'NSF_ID': '000921149', 'StartDate': '07/01/2024', 'EndDate': None, 'RoleCode': 'Principal Investigator'}

{'Name': 'New York University', 'CityName': 'NEW YORK', 'ZipCode': '100121019', 'PhoneNumber': '2129982121', 'StreetAddress': '70 WASHINGTON SQ S', 'StreetAddress2': None, 'CountryName': 'United States', 'StateName': 'New York', 'StateCode': 'NY', 'CONGRESSDISTRICT': '10', 'CONGRESS_DISTRICT_ORG': 'NY10', 'ORG_UEI_NUM': 'NX9PXMKW5KW8', 'ORG_LGL_BUS_NAME': 'NEW YORK UNIVERSITY', 'ORG_PRNT_UEI_NUM': None}

{'Name': 'New York University', 'CityName': 'NEW YORK', 'StateCode': 'NY', 'ZipCode': '100121019', 'StreetAddress': '70 WASHINGTON SQ S', 'CountryCode': 'US', 'CountryName': 'United States', 'StateName': 'New York', 'CountryFlag': '1', 'CONGRESSDISTRICT': '10', 'CONGRESS_DISTRICT_PERF': 'NY10'}

{'Code': '132000', 'Text': 'Economics'}

2023~91100

{'url': 'https://www.nsf.gov/awardsearch/download?DownloadFileName=2024&All=true', 'xml': '2437062.xml'}

Travel: Support for Conference Participation at the ACM Conference on Intelligent User Interfaces 2025

NSF

11/01/2024

10/31/2025

{'Value': 'Standard Grant'}

{'Code': '05020000', 'Directorate': {'Abbreviation': 'CSE', 'LongName': 'Direct For Computer & Info Scie & Enginr'}, 'Division': {'Abbreviation': 'IIS', 'LongName': 'Div Of Information & Intelligent Systems'}}

{'SignBlockName': 'Dan Cosley', 'PO_EMAI': 'dcosley@nsf.gov', 'PO_PHON': '7032928832'}

This award will support student attendance at the 2025 version of the Intelligent User Interfaces (IUI) conference to be held in Cagliary, Italy. IUI is the main international conference where researchers in human computer interaction (HCI) and artificial intelligence (AI) present work on systems that use AI to help people be more effective in interacting with computers. IUI research spans a wide range of socially important topics and domains, including recommender systems, adaptive educational systems, accessibility, autonomous systems, creativity, virtual agents, privacy and security of intelligent interfaces, and many others. Providing travel funding that allows junior researchers to present their own work and be enriched by others' work will strengthen the IUI community and provide valuable benefits for the students involved.<br/><br/>The funding will support around 14 students to attend the conference. Most of these students will also attend a Doctoral Consortium at which later-stage PhD students will present and get feedback on their dissertation research from both senior researchers and other PhD students in the IUI community. The availability of travel funding will be widely announced in the relevant communities, with students being invited to submit applications describing their doctoral work. These applications will be reviewed by a program committee that gives substantive feedback for every application, whether accepted or not. Acceptance decisions will be made with an eye toward topical, institutional, and demographic diversity of participants, while also considering financial need. Chosen students will attend a day-long doctoral consortium, have additional opportunities to meet with individual mentors, and be invited to present in a poster session for the conference.<br/><br/>This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

07/31/2024

07/31/2024

None

Grant

47.070

1

4900

4900

2437064

{'FirstName': 'Yong', 'LastName': 'Zheng', 'PI_MID_INIT': None, 'PI_SUFX_NAME': None, 'PI_FULL_NAME': 'Yong Zheng', 'EmailAddress': 'yzheng66@iit.edu', 'NSF_ID': '000728942', 'StartDate': '07/31/2024', 'EndDate': None, 'RoleCode': 'Principal Investigator'}

{'Name': 'Illinois Institute of Technology', 'CityName': 'CHICAGO', 'ZipCode': '606163717', 'PhoneNumber': '3125673035', 'StreetAddress': '10 W 35TH ST', 'StreetAddress2': None, 'CountryName': 'United States', 'StateName': 'Illinois', 'StateCode': 'IL', 'CONGRESSDISTRICT': '01', 'CONGRESS_DISTRICT_ORG': 'IL01', 'ORG_UEI_NUM': 'E2NDENMDUEG8', 'ORG_LGL_BUS_NAME': 'ILLINOIS INSTITUTE OF TECHNOLOGY', 'ORG_PRNT_UEI_NUM': None}

{'Name': 'Illinois Institute of Technology', 'CityName': 'CHICAGO', 'StateCode': 'IL', 'ZipCode': '606163717', 'StreetAddress': '10 W 35TH ST', 'CountryCode': 'US', 'CountryName': 'United States', 'StateName': 'Illinois', 'CountryFlag': '1', 'CONGRESSDISTRICT': '01', 'CONGRESS_DISTRICT_PERF': 'IL01'}

{'Code': '736700', 'Text': 'HCC-Human-Centered Computing'}

2024~25245

{'url': 'https://www.nsf.gov/awardsearch/download?DownloadFileName=2024&All=true', 'xml': '2437064.xml'}

Unravelling the Origin of the Matter-Antimatter Asymmetry in the Universe

NSF

08/01/2024

05/31/2027

{'Value': 'Standard Grant'}

{'Code': '03010000', 'Directorate': {'Abbreviation': 'MPS', 'LongName': 'Direct For Mathematical & Physical Scien'}, 'Division': {'Abbreviation': 'PHY', 'LongName': 'Division Of Physics'}}

{'SignBlockName': 'Bogdan Mihaila', 'PO_EMAI': 'bmihaila@nsf.gov', 'PO_PHON': '7032928235'}

Even with the extraordinary success of the the Standard Model of Particle Physics (SM), certain phenomena observed in the Universe that have profound consequences to life as we know it, such as the matter-antimatter asymmetry, the hierarchy problem, and the existence of dark matter and dark energy, remain unexplained. Therefore any physical description of such phenomena requires a theory that goes beyond the Standard Model (BSM), while at the same time encompassing the Standard Model and its predictions related to ordinary matter. A physical quantity potentially sensitive to the source responsible of the matter-antimatter asymmetry is the electric dipole moment (EDM) of particles such as the neutron and proton. The PI will study the impact of theories beyond the Standard Model to the matter-antimatter asymmetry in the universe by calculating the electric dipole moments of protons and neutrons induced by such theories. In addition to investigating the role played by theories beyond the Standard Model to the observed matter-antimatter asymmetry in the universe, which is one of the biggest unanswered questions in particle and nuclear physics, the PI will mentor a student engaged in this research.<br/><br/>This project uses a new method, based on the so-called gradient flow, for the determination of the Quantum Chromodynamics (QCD) component of key BSM matrix elements related to quark and strong theta-CP violations. This set of matrix elements impacts the understanding of electric dipole moments (EDMs) within nucleons and nuclei (a key signature of BSM physics), and their determination will lay the foundation for extraction of BSM observables from future low-energy, high-intensity experimental measurements. The use of the gradient flow will circumvent some of the big challenges posed by the determination of the above-mentioned matrix elements by introducing a new scale, the flow time, that will mitigate divergences present in the calculations. Additionally the gradient flow is perfectly suited to be adopted to QCD calculations on the lattice. Lattice QCD is, as today, the only robust and theoretically sound approach to non-perturbative QCD calculations. The new method the PI has developed is ideally suited for calculating all the CP-violating contributions to the EDM of nucleons and light nuclei. Most of the tools and technique developed in this project are alternative to traditional methods and can be easily applied to other matrix element calculations contributing to the study of dark matter candidates.<br/><br/>This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

07/17/2024

07/17/2024

None

Grant

47.049

1

4900

4900

2437065

{'FirstName': 'Andrea', 'LastName': 'Shindler', 'PI_MID_INIT': None, 'PI_SUFX_NAME': None, 'PI_FULL_NAME': 'Andrea Shindler', 'EmailAddress': 'shindler@frib.msu.edu', 'NSF_ID': '000737114', 'StartDate': '07/17/2024', 'EndDate': None, 'RoleCode': 'Principal Investigator'}

{'Name': 'University of California-Berkeley', 'CityName': 'BERKELEY', 'ZipCode': '947101749', 'PhoneNumber': '5106433891', 'StreetAddress': '1608 4TH ST STE 201', 'StreetAddress2': None, 'CountryName': 'United States', 'StateName': 'California', 'StateCode': 'CA', 'CONGRESSDISTRICT': '12', 'CONGRESS_DISTRICT_ORG': 'CA12', 'ORG_UEI_NUM': 'GS3YEVSS12N6', 'ORG_LGL_BUS_NAME': 'REGENTS OF THE UNIVERSITY OF CALIFORNIA, THE', 'ORG_PRNT_UEI_NUM': None}

{'Name': 'University of California-Berkeley', 'CityName': 'BERKELEY', 'StateCode': 'CA', 'ZipCode': '947101749', 'StreetAddress': '1608 4TH ST STE 201', 'CountryCode': 'US', 'CountryName': 'United States', 'StateName': 'California', 'CountryFlag': '1', 'CONGRESSDISTRICT': '12', 'CONGRESS_DISTRICT_PERF': 'CA12'}

{'Code': '128500', 'Text': 'NUCLEAR THEORY'}

2022~270000

{'url': 'https://www.nsf.gov/awardsearch/download?DownloadFileName=2024&All=true', 'xml': '2437065.xml'}

Nonlinear Stochastic Partial Differential Equations and Applications

NSF

06/01/2024

08/31/2026

{'Value': 'Standard Grant'}

{'Code': '03040000', 'Directorate': {'Abbreviation': 'MPS', 'LongName': 'Direct For Mathematical & Physical Scien'}, 'Division': {'Abbreviation': 'DMS', 'LongName': 'Division Of Mathematical Sciences'}}

{'SignBlockName': 'Pedro Embid', 'PO_EMAI': 'pembid@nsf.gov', 'PO_PHON': '7032924859'}

Statistical uncertainty plays a significant role in a diverse range of models for complicated dynamic phenomena, leading to wild, stochastic behavior. Such probabilistic effects are caused, for instance, by unpredictable market shifts in the global economy, or turbulent or chaotic weather patterns at the evolving front of a massive forest fire. The investigator will develop a mathematical understanding for the equations arising in these applications, while also studying the stabilizing and regularizing effects of stochastic noise, for which there is often experimental or numerical evidence. This project will generate opportunities to mentor graduate and undergraduate students by providing both professional advice and mathematical knowledge related to the project. <br/><br/>Dynamical random behavior under various complex influences is often described by nonlinear stochastic partial differential equations. Such equations cannot be solved through the superposition of simple formulae and are therefore not yet well-understood mathematically. The project will draw on tools from functional analysis and probability to resolve the well-posedness of nonlinear stochastic partial differential equations arising in competitive large population dynamics and in stochastically forced interface evolutions. The effects of stochasticity will be further analyzed by studying the long-time behavior of solutions, probabilistic averaging and regularizing phenomena, and stochastic selection principles for models with a small level of background noise. The material influence of stochasticity indicates that the statistical fluctuations in experimental data cannot be completely ignored, thereby justifying the technical study of those stochastic partial differential equations involved in this project.<br/><br/>This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

07/01/2024

07/01/2024

None

Grant

47.049

1

4900

4900

2437066

{'FirstName': 'Benjamin', 'LastName': 'Seeger', 'PI_MID_INIT': None, 'PI_SUFX_NAME': None, 'PI_FULL_NAME': 'Benjamin Seeger', 'EmailAddress': 'seeger@math.utexas.edu', 'NSF_ID': '000783123', 'StartDate': '07/01/2024', 'EndDate': None, 'RoleCode': 'Principal Investigator'}

{'Name': 'University of North Carolina at Chapel Hill', 'CityName': 'CHAPEL HILL', 'ZipCode': '275995023', 'PhoneNumber': '9199663411', 'StreetAddress': '104 AIRPORT DR STE 2200', 'StreetAddress2': None, 'CountryName': 'United States', 'StateName': 'North Carolina', 'StateCode': 'NC', 'CONGRESSDISTRICT': '04', 'CONGRESS_DISTRICT_ORG': 'NC04', 'ORG_UEI_NUM': 'D3LHU66KBLD5', 'ORG_LGL_BUS_NAME': 'UNIVERSITY OF NORTH CAROLINA AT CHAPEL HILL', 'ORG_PRNT_UEI_NUM': 'D3LHU66KBLD5'}

{'Name': 'University of North Carolina at Chapel Hill', 'CityName': 'CHAPEL HILL', 'StateCode': 'NC', 'ZipCode': '275995023', 'StreetAddress': '104 AIRPORT DR STE 2200', 'CountryCode': 'US', 'CountryName': 'United States', 'StateName': 'North Carolina', 'CountryFlag': '1', 'CONGRESSDISTRICT': '04', 'CONGRESS_DISTRICT_PERF': 'NC04'}

{'Code': '126600', 'Text': 'APPLIED MATHEMATICS'}

2023~169408

{'url': 'https://www.nsf.gov/awardsearch/download?DownloadFileName=2024&All=true', 'xml': '2437066.xml'}

Collaborative Research: Central tropical Pacific climate variability over the Last Millennium

NSF

06/15/2024

06/30/2025

{'Value': 'Standard Grant'}

{'Code': '06040200', 'Directorate': {'Abbreviation': 'GEO', 'LongName': 'Directorate For Geosciences'}, 'Division': {'Abbreviation': 'OCE', 'LongName': 'Division Of Ocean Sciences'}}

{'SignBlockName': 'Gail Christeson', 'PO_EMAI': 'gchriste@nsf.gov', 'PO_PHON': '7032922952'}

This award is funded in whole or in part under the American Rescue Plan Act of 2021 (Public Law 117-2). The project will assemble radiocarbon-dated segments of ancient coral recovered from islands in the central equatorial Pacific to study climate conditions during the last 1000 years. It will focus on two important time periods, the Medieval Climate Anomaly (850-1300 ago) and the Little Ice Age (1400-1850) to look at what factors cause El Nino conditions. Researchers will reconstruct monthly-resolution records of seawater temperature and salinity conditions by targeting growth bands in coral skeletons. This project supports research training educational opportunities for several undergraduate and graduate students. It also supports a research collaboration among two early career scientists and others who have a strong track record of participation in K-16 education and public outreach with an emphasis on broadening diversity and inclusion of underrepresented communities. These efforts will continue with guest lectures at a nearby minority-serving institution and by offering summer research opportunities for local high school students.<br/><br/>Improving the accuracy of future climate projections requires a more complete understanding of internal vs. externally forced changes in tropical Pacific climate on a broad range of timescales. Coral-based paleoclimate records have dramatically improved our understanding of interannual climate variability in the tropical Pacific, however, similar insights into centennial-scale variability have thus far remained unattainable due to (i) diagenesis, which can significantly bias coral reconstructions, and (ii) colony-to- colony offsets in coral proxies, which introduce large uncertainties in estimates of mean climate change from single corals. Informed by two decades of work at Kiritimati Island (2degN, 157degW), this project will generate ~50 new well-dated and multi-proxy records of climate variability across the last millennium. Using a novel approach that layers paired coral oxygen isotope and Sr/Ca measurements, with a new paleothermometer, Sr-U, this reconstruction will provide the first set of robust, quantitative, and independent estimates of central tropical Pacific surface temperature (SST) and hydroclimate trends across the Medieval Climate Anomaly (MCA; 900-1200CE) and Little Ice Age (LIA; 1500-1800CE). The fidelity of these fossil coral records will be further ensured using rigorous screening for diagenesis and a microscale analyses to extract reliable climate information from altered corals. Comparisons of these new reconstructions with transient climate simulations will provide much-needed context for present-day trends and allow us to investigate the tropical Pacific’s long-term response to external forcings and climate feedbacks. Broader impacts to this project include a dramatic improvement to our understanding of natural climate variability in the tropical Pacific, allowing for the better quantification of regional anthropogenic climate trends, and the improvement of future climate projections by providing more accurate benchmarks for climate models. As climate change continues to dominate social consciousness, the PI will actively engage in public outreach efforts to communicate the results of the paleoclimate research as well as more general information about local/regional impacts of future climate change. It supports research training and mentoring of several graduate students and summer research experiences for high school students from a nearby school district which has a >90% underrepresented minority student population.<br/><br/>This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

07/18/2024

07/22/2024

None

Grant

47.050

1

4900

4900

2437076

[{'FirstName': 'Alyssa', 'LastName': 'Atwood', 'PI_MID_INIT': None, 'PI_SUFX_NAME': None, 'PI_FULL_NAME': 'Alyssa Atwood', 'EmailAddress': 'aatwood@fsu.edu', 'NSF_ID': '000708393', 'StartDate': '07/22/2024', 'EndDate': None, 'RoleCode': 'Principal Investigator'}, {'FirstName': 'Kim', 'LastName': 'Cobb', 'PI_MID_INIT': 'M', 'PI_SUFX_NAME': None, 'PI_FULL_NAME': 'Kim M Cobb', 'EmailAddress': 'kim_cobb@brown.edu', 'NSF_ID': '000090921', 'StartDate': '07/18/2024', 'EndDate': '07/22/2024', 'RoleCode': 'Former Principal Investigator'}]

{'Name': 'Florida State University', 'CityName': 'TALLAHASSEE', 'ZipCode': '323060001', 'PhoneNumber': '8506445260', 'StreetAddress': '874 TRADITIONS WAY', 'StreetAddress2': None, 'CountryName': 'United States', 'StateName': 'Florida', 'StateCode': 'FL', 'CONGRESSDISTRICT': '02', 'CONGRESS_DISTRICT_ORG': 'FL02', 'ORG_UEI_NUM': 'JF2BLNN4PJC3', 'ORG_LGL_BUS_NAME': 'FLORIDA STATE UNIVERSITY', 'ORG_PRNT_UEI_NUM': None}

{'Name': 'Florida State University', 'CityName': 'TALLAHASSEE', 'StateCode': 'FL', 'ZipCode': '323060001', 'StreetAddress': '874 TRADITIONS WAY', 'CountryCode': 'US', 'CountryName': 'United States', 'StateName': 'Florida', 'CountryFlag': '1', 'CONGRESSDISTRICT': '02', 'CONGRESS_DISTRICT_PERF': 'FL02'}

{'Code': '162000', 'Text': 'Marine Geology and Geophysics'}

2021~482354

{'url': 'https://www.nsf.gov/awardsearch/download?DownloadFileName=2024&All=true', 'xml': '2437076.xml'}

Planning: CRISES: National Center for Water Policy (NCWP)

NSF

09/15/2024

12/31/2025

{'Value': 'Standard Grant'}

{'Code': '04010000', 'Directorate': {'Abbreviation': 'SBE', 'LongName': 'Direct For Social, Behav & Economic Scie'}, 'Division': {'Abbreviation': 'SMA', 'LongName': 'SBE Off Of Multidisciplinary Activities'}}

{'SignBlockName': 'Tom Evans', 'PO_EMAI': 'tevans@nsf.gov', 'PO_PHON': '7032924891'}

A significant portion of the world’s population does not have access to safe sanitation facilities, and thirty percent do not have safe drinking water. Drinking water and surface water pollution remain key environmental concerns among the public. These concerns exist despite considerable public and private investment. In addition to traditional pollution threats, newer concerns about microplastics and “forever chemicals” such as perfluoroalkyl and polyfluoroalkyl substances (PFAS) have emerged. At the same time, water quantity crises ranging from too much to too little are emerging as some of the most vital resource issues of the 21st century challenging human health and food security. This project will investigate how to characterize and quantify the full range of economic costs and benefits, effectiveness, and equity of water resource policies. This foundation will inform how to guide investments in infrastructure to protect against flooding, minimize impairments of waterways and contamination of drinking water supplies, address the inequitable distribution of clean water, and encourage the development and deployment of clean water technologies.<br/><br/>This project outlines plans to develop a National Center for Water Policy (NCWP). A series of workshops are convened with a multidisciplinary group of researchers and practitioners to produce (1) a detailed plan for a National Center for Water Policy; (2) a team-authored policy paper that synthesizes expert opinion on the water resource issues, challenges, and research needs in the United States; and (3) a synthesis of academic and policy discussions related to the current state of water policy in the United States. This project develops a convergent approach to understanding the full impact of the nation’s most pressing water quality and quantity challenges on human wellbeing, synthesizes findings from harmonized research to better understand effective and equitable policy solutions, and builds capacity for knowledge transfer and policy outreach to facilitate the use of leading-edge research in policy solutions. This project investigates the potential for a NCWP to coordinate and conduct research on the broader water quality and quantity challenges.<br/><br/>This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

08/19/2024

08/19/2024

None

Grant

47.075

1

4900

4900

2437086

{'FirstName': 'David', 'LastName': 'Keiser', 'PI_MID_INIT': None, 'PI_SUFX_NAME': None, 'PI_FULL_NAME': 'David Keiser', 'EmailAddress': 'dkeiser@umass.edu', 'NSF_ID': '000691177', 'StartDate': '08/19/2024', 'EndDate': None, 'RoleCode': 'Principal Investigator'}

{'Name': 'University of Massachusetts Amherst', 'CityName': 'AMHERST', 'ZipCode': '010039252', 'PhoneNumber': '4135450698', 'StreetAddress': '101 COMMONWEALTH AVE', 'StreetAddress2': None, 'CountryName': 'United States', 'StateName': 'Massachusetts', 'StateCode': 'MA', 'CONGRESSDISTRICT': '02', 'CONGRESS_DISTRICT_ORG': 'MA02', 'ORG_UEI_NUM': 'VGJHK59NMPK9', 'ORG_LGL_BUS_NAME': 'UNIVERSITY OF MASSACHUSETTS', 'ORG_PRNT_UEI_NUM': None}

{'Name': 'University of Massachusetts Amherst', 'CityName': 'AMHERST', 'StateCode': 'MA', 'ZipCode': '010039252', 'StreetAddress': '101 COMMONWEALTH AVE', 'CountryCode': 'US', 'CountryName': 'United States', 'StateName': 'Massachusetts', 'CountryFlag': '1', 'CONGRESSDISTRICT': '02', 'CONGRESS_DISTRICT_PERF': 'MA02'}

{'Code': '265Y00', 'Text': 'CRISES-R&I in Sci, Env&Society'}

2024~99990

{'url': 'https://www.nsf.gov/awardsearch/download?DownloadFileName=2024&All=true', 'xml': '2437086.xml'}

COLLABORATIVE RESEARCH: DMREF: Designing Plasmonic Nanoparticle Assemblies For Active Nanoscale Temperature Control By Exploiting Near- And Far-Field Coupling

NSF

08/01/2024

07/31/2025

{'Value': 'Standard Grant'}

{'Code': '03090000', 'Directorate': {'Abbreviation': 'MPS', 'LongName': 'Direct For Mathematical & Physical Scien'}, 'Division': {'Abbreviation': 'CHE', 'LongName': 'Division Of Chemistry'}}

{'SignBlockName': 'Suk-Wah Tam-Chang', 'PO_EMAI': 'stamchan@nsf.gov', 'PO_PHON': '7032928684'}

With the support of the DMREF Program and the Division of Chemistry, Professor David J. Masiello from the University of Washington, Professor Stephan Link from Rice University, and Professor Katherine A. Willets from Temple University are developing methods to theoretically design and experimentally realize a new class of periodic 1D and 2D thermal metamaterials. Thermal energy, or heat, flows naturally from hot to cold, making it difficult to create localized thermal “hot spots” even when heat is applied to a single location. Said differently, the degree of spatial correlation between the heat power supplied and the temperature change that it induces is likely to be small. Touching a hot pan’s lid provides a simple and all too familiar example of this effect. As a material’s size is reduced to 10-100s of nanometers, or about 1,000 times smaller than the width of a human hair, depositing and maintaining thermal energy within a small region of space becomes even more challenging. Yet, the ability to control heat flow and thus temperature at both nanoscale (<100 nm) and micron-scale (~1-100 μm) dimensions has important implications for applications ranging from big data to nanomedicine. This research project aims to overcome thermal diffusion and achieve long-range global control of spatially-nonuniform heating, using only light to actively control the thermal profile of the materials. Beyond impacting a wide variety of applications, the project will facilitate the interdisciplinary training of students and postdoctoral researchers through student exchange between the three research groups, organization of two new scientific meetings, and the design of a nanotechnology summer camp for middle school students with focus on photothermal materials.<br/><br/>The goal of this project is to overcome thermal diffusion through the theoretical design and experimental realization of a new class of periodic 1D and 2D thermal metamaterials. Plasmonic nanoparticle unit cells that are individually capable of hosting spatially-controllable nanoscale thermal profiles will be integrated into periodic lattices, which introduces the possibility for long-range global control of spatially-nonuniform heating upon optical excitation. To achieve this goal, the research team will (i) expand the design and thermal characterization capabilities for multi-particle unit cells that exploit near-field coupling; (ii) engineer photonic band structure to sculpt long-range thermal profiles in 1D and 2D Bravais lattices using light; and (iii) integrate multiple sub-lattices to realize 1D and 2D non-Bravais lattices to actively control both nanoscale and micron-scale thermal profiles using light. Realization of such thermally-active materials will require the coordinated and iterative efforts of a highly-skilled team capable of integrating new theoretical methods for predicting how light energy is transduced into modified thermal profiles with experimental fabrication and characterization techniques to design and quantify temperature across decades of length scales, spanning from below the diffraction limit to millimeters. This project will leverage the iterative theory-experiment-theory feedback loop to expand the genome of actively-controllable photothermal materials.<br/><br/>This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

07/29/2024

07/29/2024

None

Grant

47.049