The National Science Foundation awarded a $500,000 Project Grant to the George Washington University under the Computer and Information Science and Engineering program (CFDA 47.070). The three-year award will support research into the holistic design of high-performance and energy-efficient accelerators for graph neural networks from October 1, 2021 through September 30, 2024. As described under the CFDA program, funding will advance the development of computing and communications...
This $600,000 National Science Foundation (NSF) Project Grant under the Computer and Information Science and Engineering (CFDA 47.070) program aims to enhance machine learning capabilities when dealing with data distribution shifts, particularly in graph-structured data from particle physics and biochemistry applications. The award to Georgia Tech Research Corp. has two main research thrusts: 1) Developing methods to estimate and mitigate shifts in graph structure for tasks like node...
The National Science Foundation (NSF) Computer and Information Science and Engineering (CISE) Federal Grant Program (CFDA 47.070) awarded Massachusetts Institute of Technology (MIT) a $600,000 Project Grant for the period of March 1, 2025 to February 29, 2028. The grant supports research to develop a mathematical foundation for leveraging graph data in machine learning tasks. Key focus areas include characterizing how the geometry of the underlying latent space affects graph structure,...
The University of Virginia (UVA) received a $600,000 Project Grant from the National Science Foundation's (NSF) Computer and Information Science and Engineering (CISE) Program (CFDA 47.070) to advance federated graph machine learning (FGML) techniques. The project aims to 1) address data heterogeneity challenges in FGML, 2) develop novel algorithms to tackle label deficiency issues, and 3) strengthen data privacy protection for node attributes and graph structures. The research will produce...
This Project Grant award from the National Science Foundation's (NSF) Division of Mathematical Sciences (CFDA 47.049 - Mathematical and Physical Sciences) supports research to develop novel mathematical models and efficient algorithms for deep learning on large-scale graph-structured data. The $249,999 award, spanning September 2024 to August 2027, aims to produce innovations in areas like graph convolutional networks, graph matching, and graph clustering. The research will involve graduate...
The National Science Foundation (NSF) awarded a $484,822 Project Grant through its Computer and Information Science and Engineering (CFDA #47.070) program to the University of Chicago. This 5-year grant, effective July 1, 2023, supports research into characterizing the properties, reliability, and sensitivity of graph neural networks (GNNs) and advancing the theoretical understanding of statistical properties in graph estimators. The goal is to transform GNNs from black-box models into...
The National Science Foundation awarded a $515,999 Project Grant to the University of Notre Dame under the Computer and Information Science and Engineering federal grant program (CFDA 47.070) to develop comprehensive methods for learning to augment graph data through machine learning algorithms. Over a three-year period from March 2022 to February 2025, the University will deliver novel techniques to augment graph data by counterfactual inference on edges as treatment variables, forecasting...
This federal Project Grant award from the National Science Foundation's (NSF) Computer and Information Science and Engineering (CISE) program (CFDA 47.070) provides $631,953 to Yale University to develop a general foundation model framework for graph-structured data in scientific discovery. The researchers will address key limitations in existing graph foundation models by incorporating novel approaches such as multi-level graph neural networks, graph signal processing, multimodal graph...
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 $599,999 Project Grant award from the National Science Foundation's Computer and Information Science and Engineering program (CFDA 47.070) supports the development of scalable and accurate solutions for temporal graph machine learning (TGML). The project aims to create a robust cyber infrastructure toolkit that enables efficient training and inference of TGML models, allowing researchers and practitioners to analyze large-scale temporal graphs with improved accuracy and scalability. The...