Project Grant 2521631
Award Date 9/1/25
Completion Date 8/31/28
Dollars Obligated $300K
Federal Agency
Federal Grant Program
Assistance Type
Project Grant
Place of Performance
Austin, TX 78712, USA
Similar Awards
- This $399,574 Project Grant awarded by the National Science Foundation's (NSF) Social, Behavioral, and Economic Sciences (CFDA 47.075) program supports research to assess and mitigate spatial biases in large-scale mobile location data used for human mobility analysis. The University of Florida, the prime awardee, will undertake four key research tasks: 1) quantify spatial bias in mobile location data; 2) identify causes of spatial biases from the data generation process; 3) develop new methods...
- This $399,162 Project Grant award from the National Science Foundation's Geosciences Program (CFDA 47.050) aims to develop interpretable, stable, and mass-conserving artificial intelligence (AI) models to improve the computational speed and efficiency of geoscientific models, such as those used for air pollution and climate research. The project will create simpler "surrogate" machine learning models for key components like atmospheric chemistry and wildfire plume rise, allowing for...
- This $600,000 Project Grant award from the National Science Foundation's (NSF) Computer and Information Science and Engineering (CFDA 47.070) program supports research to enhance the adversarial robustness of geospatial-temporal AI models. The primary objectives are to examine vulnerabilities of existing models to adversarial attacks and develop effective solutions to strengthen their resilience. The research team at Michigan State University will work to proactively identify and mitigate...
- This $199,944 federal Project Grant award from the National Science Foundation's Social, Behavioral, and Economic Sciences program (CFDA 47.075) aims to develop advanced explainable spatial machine learning models and algorithms for clustering and predicting complex socioeconomic data. The project will create innovative, interpretable spatial machine learning methods applicable to a range of real-world problems in fields like public safety, social sciences, and advanced manufacturing. Key...
- This $600,000 Project Grant from the National Science Foundation's Computer and Information Science and Engineering program will fund research at the University of Maryland, College Park to develop transparent and interpretable bias mitigation approaches for place-based mobility-centric prediction models. The three-year award beginning September 1, 2022 will support technical contributions in three thrusts. The first will provide a novel prediction model to forecast reported place-based...
- This $431,480 federal Project Grant award from the National Science Foundation's (NSF) Mathematical and Physical Sciences program (CFDA 47.049) will support a research team at the University of Michigan in mitigating bias in artificial intelligence (AI) systems used in astronomy and medical imaging. The key objectives are to: 1) identify, characterize, and mitigate "gold standard" labeling bias in galaxy shape classifications, which can impact downstream AI models; and 2) apply the...
- The National Science Foundation awarded $755,098 under the Computer and Information Science and Engineering federal grant program to the University of Pittsburgh to advance deep learning methods towards spatial fairness from June 2022 to May 2025. The University will develop new statistical formulations and machine learning frameworks to explicitly preserve spatial fairness when applying artificial intelligence to geospatial problems. It will investigate challenges in partitioning spatial data...
- This $175,000 Project Grant award from the National Science Foundation's (NSF) Mathematical and Physical Sciences program (CFDA 47.049) supports the development of novel Bayesian statistical frameworks to address measurement error challenges in complex multivariate data. The project aims to create more flexible, data-driven methods that can reliably identify meaningful patterns and relationships from noisy, imprecise observations - a common issue in fields like health research, astronomy, and...
- The National Science Foundation (NSF) Computer and Information Science and Engineering (CISE) Federal Grant Program (CFDA 47.070) has awarded a $100,000 project grant to the University of Oklahoma for the project "Ethical and Responsible Artificial Intelligence for Earth Sciences". This 3-year project aims to address challenges around bias in AI systems used for weather and climate modeling. Key activities include: Extending categorization of biases in AI for earth sciences to enable...
- This Project Grant award, valued at $325,000.00, was provided by the National Science Foundation's Social, Behavioral, and Economic Sciences (CFDA 47.075) program to the University of Washington. The project develops powerful new tools for understanding complex data, leveraging cutting-edge artificial intelligence (AI) techniques to help data analysts across diverse fields make informed, automated decisions. The research introduces novel approaches to analyze messy, heterogeneous, and large...