Project Grant 2219956

Award Date 9/1/23
Completion Date 8/31/26
Dollars Obligated $125K
Federal Grant Program
47.049
Assistance Type
Project Grant
Place of Performance
Salt Lake City, UT 84112, 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...
The University of Utah received a $104,794 Project Grant award from the National Science Foundation Division of Mathematical Sciences on January 15, 2021 to support collaborative research titled "ROBUST, ACCURATE AND EFFICIENT GRAPH-STRUCTURED RNN FOR SPATIO-TEMPORAL FORECASTING AND ANOMALY DETECTION." The period of performance for this award is January 15, 2021 through December 31, 2022. The award supports research under the Mathematical and Physical Sciences program (CFDA 47.049)...
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,...
This federal Project Grant award, provided by the National Science Foundation's (NSF) Mathematical and Physical Sciences program (CFDA 47.049), will support research on optimization techniques and geometrically constrained neural networks for threat detection. The $250,000 award to Colorado State University (CSU) will run from September 1, 2024, to August 31, 2027. The research will focus on adapting and tuning mathematical tools, including geometry, topology, optimization, and machine learning,...
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This three-year project grant from the National Science Foundation's Mathematical and Physical Sciences program (CFDA 47.049) provides $200,000 to support research at the University of Utah developing new deep learning algorithms for sequential and graph data motivated by differential equations theory. The grant aims to advance machine learning methodology for ubiquitous problems involving sequentially observed data from multiple agents, such as pandemic spread modeling, cooperative robotics...
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The National Science Foundation (NSF) Division of Mathematical Sciences awarded a $125,000 Project Grant to the University of Utah for the project "COLLABORATIVE RESEARCH: ATD: FAST ALGORITHMS AND NOVEL CONTINUOUS-DEPTH GRAPH NEURAL NETWORKS FOR THREAT DETECTION" under the Mathematical and Physical Sciences program (CFDA 47.049).

The project aims to advance graph neural network (GNN) algorithms for improved accuracy and efficiency in threat detection within multivariate time series data, which has applications in fields like AI, traffic analysis, power systems, and health analytics. The research focuses on three key areas: 1) developing continuous-depth GNNs based on diffusion equation theory to overcome limitations like over-smoothing, 2) creating fast algorithms for GNN and attention mechanism training, testing, and inference, and 3) applying the new algorithms to anomaly detection in benchmark graph learning tasks as well as traffic flow, power distribution, and epidemic data. The project will provide training and STEM education opportunities for underrepresented students.

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