Project Grant 2302970

Award Date 4/1/23
Completion Date 3/31/26
Dollars Obligated $124K
Federal Grant Program
47.070
Assistance Type
Project Grant
Place of Performance
Fairfax, VA, USA
Similar Awards
This Project Grant from the National Science Foundation's Division of Information and Intelligent Systems, under the Computer and Information Science and Engineering program (CFDA 47.070), provides $373,667 to Emory University for research on understanding and mitigating bias in artificial intelligence models for infectious disease spread prediction. Over a three-year period from April 2023 through March 2026, the grantee will develop an AI system to predict the spatial and temporal spread of...
This $299,862 National Science Foundation project grant under the Computer and Information Science and Engineering program will support the development of a decision support system to strengthen public health response to infectious disease outbreaks in the US and Australia. Researchers from the University of Texas at Austin and Commonwealth Scientific and Industrial Research Organisation (CSIRO) in Australia will collaborate to develop graph representation learning methods for fair teaming...
This three-year, $299,938 project grant from the National Science Foundation's Computer and Information Science and Engineering program aims to develop a decision support system and fair teaming recommendations for infectious disease response. Funded through a collaboration between the University of California, Los Angeles and the Australian Commonwealth Scientific and Industrial Research Organisation, the project will construct biomedical knowledge graphs, identify core response teams, and...
The National Science Foundation SBE Office of Multidisciplinary Activities awarded $200,151 under the Social, Behavioral, and Economic Sciences federal grant program (CFDA 47.075) to the University of California, Los Angeles for the period of September 1, 2022 through February 29, 2024. The award will support development of a prototype pandemic early warning system powered by artificial intelligence, machine learning, and open-source technologies. The system will monitor biological,...
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 $1,999,688 National Science Foundation Project Grant supports the development of predictive intelligence for pandemic prevention through transdisciplinary innovation. Funded by NSF's Computer and Information Science and Engineering program (CFDA #47.070), the two-year award to the University of California, Davis enables advancement of predictive capabilities for forecasting pandemic risk through three key research areas. First, the team will characterize environmental conditions and human...
This National Science Foundation (NSF) Computer and Information Science and Engineering (CISE) Federal Grant Award (CFDA 47.070) provides $164,940 to Emory University in Atlanta, Georgia to conduct research on ensuring fairness in artificial intelligence (AI) algorithms under real-world challenges. The project aims to investigate the impact of generalization, privacy, and robustness issues on algorithmic fairness, and develop effective solutions to address these challenges. Key research focus...
The National Science Foundation (NSF) awarded a $200,000 Project Grant under the Mathematical and Physical Sciences program (CFDA 47.049) to the University of Virginia to develop data-driven, multimodal methods for behavior-based epidemiological modeling. The key objectives are to: Improve techniques for deriving meaningful insights from imperfect, real-world sensor data like mobile phones and search engine logs to capture complex human behaviors in real-time. Couple agent-based disease models...
This Project Grant award from the National Science Foundation (CFDA 47.049 - Mathematical and Physical Sciences) provides $275,000 to the University of California, Los Angeles (UCLA) to study fairness and bias in machine learning (ML) and artificial intelligence (AI) algorithms. The key objectives are to: Create a framework to identify fairness metrics across ML/AI algorithmic pipelines and develop technologies to mitigate biases and improve fairness. Provide foundational mathematical support...
This $597,149 Project Grant awarded by the National Science Foundation (NSF) under the Computer and Information Science and Engineering (CISE) program (CFDA 47.070) aims to advance fundamental research in fair algorithmic decision-making. The project at Purdue University will develop novel algorithms and software to facilitate the adoption and evaluation of fair artificial intelligence (AI) systems, with a focus on promoting health equity in applications like Alzheimer's disease research. Key...

This Project Grant from the National Science Foundation's Division of Information and Intelligent Systems will fund $123,946 for research into understanding and mitigating bias in artificial intelligence models for predicting infectious disease spread. The award is part of the Computer and Information Science and Engineering program (CFDA 47.070), which supports investigator-initiated research and education in computing, communications, and information science and engineering.

Specifically, the joint U.S.-Australia research team will develop an AI system to predict emerging infectious disease spread over space and time. They will also use agent-based simulation to inject controlled bias into collected data on population mobility patterns. Finally, the researchers will analyze how different types of simulated data bias lead to biased AI predictions, studying fairness metrics that can be incorporated into the AI optimization process to mitigate bias. The overall goal is to enable accurate, scalable and rapid pandemic predictions through identifying, measuring and reducing inherent bias in traditional AI approaches. George Mason University will receive the funding to support this three-year project ending March 2026.

Generated 1/6/24, 11:42 AM