Project Grant W911NF2010146

The National Science Foundation (NSF) awarded a $198,688 Project Grant under the Social, Behavioral, and Economic Sciences (CFDA 47.075) program to the University of Notre Dame. The "Collaborative Research: HNDS-R: Advancing Diverse Viable Effective Networked Teams (ADVENT)" project aims to create an open-source computational network model to optimize team formation by integrating functional individual differences and member affinity. The researchers will train and test this model...

This National Science Foundation (NSF) Social, Behavioral, and Economic Sciences (CFDA 47.075) Project Grant award of $200,592 to Northwestern University will fund the "Collaborative Research: HNDS-R: Advancing Diverse Viable Effective Networked Teams (ADVENT)" project. The research aims to create an open-source computational network model to optimize team formation by integrating functional individual differences and member affinity. The researchers will train and test this model...

This Project Grant award from the National Science Foundation's (NSF) Computer and Information Science and Engineering (CISE) program provides $200,000 to Emory University to develop new methods for modeling the dynamics of brain graphs derived from neuroimaging data. The 3-year project, starting on October 1, 2023, aims to create a unified framework of brain graph ordinary differential equations (BrainGDE) that integrates advanced deep graph learning techniques and ordinary differential...

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 National Science Foundation (NSF) Computer and Information Science and Engineering (CISE) Program (CFDA 47.070) project grant awards $600,000 to the Rector & Visitors of the University of Virginia (University of Virginia) to develop innovative approaches for efficient training of Dynamic Graph Neural Network (DGNN) models on large-scale, time-varying graphs. The 3-year project, from October 2024 to September 2027, aims to create novel methods for graph partitioning, sampling, caching,...

This $299,574 federal Project Grant awarded by the National Science Foundation's Computer and Information Science and Engineering (CISE) program (CFDA 47.070) aims to develop a comprehensive cyberinfrastructure solution for training large-scale Graph Neural Networks (GNNs) to support spatiotemporal prediction and modeling of geographically distributed and heterogeneous data. The project led by Emory University will address key research challenges in formulating spatiotemporal prediction within a...

Virginia Polytechnic Institute & State University (Virginia Tech) was awarded a $342,512 Project Grant from the National Science Foundation Division of Mathematical Sciences under the Mathematical and Physical Sciences program (CFDA 47.049). The grant will support research from August 2021 through July 2024 titled "ATD: Inductive Spatiotemporal Graph Encoding for Interpretable and Transferable Deep Learning with Application in Human Dynamics." The research aims to develop new...

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...

This three-year, $532,241 Project Grant from the National Science Foundation's Division of Computer and Network Systems, under the Computer and Information Science and Engineering program (CFDA 47.070), will support the development of scalable algorithms, systems, and infrastructures for graph neural network training. The University of Massachusetts will develop a novel "split parallelism" training paradigm to transparently scale graph neural network training to large-scale graphs...

This $599,573 federal Project Grant award from the National Science Foundation (NSF) Computer and Information Science and Engineering (CISE) program supports the University of Notre Dame's research to develop a new machine learning paradigm for effective yet efficient foundation graph learning models (FGLMs). The project aims to create techniques, methods, and models for FGLMs that can be widely applied in areas like scientific research, social network analysis, anomaly detection, drug...