Project Grant 2427955

Award Date 9/1/24
Completion Date 8/31/27
Dollars Obligated $180K
Federal Grant Program
47.049
Assistance Type
Project Grant
Place of Performance
Minneapolis, MN 55455, USA
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This $125,000 Project Grant awarded by the National Science Foundation (NSF) Division of Mathematical Sciences under the Mathematical and Physical Sciences program (CFDA 47.049) supports a collaborative research project on developing improved graph neural network (GNN) algorithms for threat detection. The key research objectives are to: 1) maintain accuracy with deep GNNs, 2) enable GNN training with limited data, and 3) reduce computational costs for training and deploying deep GNNs with...
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This Project Grant award for $180,000.00, provided by the National Science Foundation (NSF) under the Mathematical and Physical Sciences federal grant program (CFDA 47.049), aims to advance the mathematical understanding of trustworthy artificial intelligence (AI) algorithms for threat detection.

The primary objectives are to investigate few-shot learning techniques, which can build effective models from a very limited number of data samples, and to explore few-shot graph generation methods, crucial for modeling social and transportation networks relevant to threat detection scenarios. The research focuses on improving the transparency, robustness, and accuracy of AI-based threat detection systems, which is a key challenge in critical applications. The award was made to the Regents of the University of Minnesota, a prominent land-grant research institution, and will support this work over a 3-year period from September 2024 to August 2027.

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