Project Grant 2326494
Award Date
10/1/23
Completion Date
9/30/26
Dollars Obligated
$300K
Federal Agency
Division of Computing and Communication Foundations
Federal Grant Program
47.070
Assistance Type
Project Grant
Place of Performance
Rochester, NY 14611, USA
Similar Awards
This $266,000 Project Grant award from the National Science Foundation's Computer and Information Science and Engineering (CFDA 47.070) program aims to develop novel graph neural network (GNN) models that can effectively compress large, complex graphs while preserving their structural information. The primary objectives are to: Create graph compression techniques to address the oversmoothing problem common in GNNs. Develop compression methods that capture local, global, and higher-order...
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 (NSF) awarded a $469,787 Project Grant to Trustees of Boston University under the Computer and Information Science and Engineering (CFDA 47.070) program. The grant funds the design and development of GNNSuite, a novel unified framework for scaling graph machine learning workloads on modern storage technology. Key project objectives include methods and tools for training and serving large graph neural network (GNN) models on larger-than-memory graphs without...
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...
The National Science Foundation (NSF) awarded a $175,000 Computer and Information Science and Engineering (CISE) Program grant to The Trustees of the Stevens Institute of Technology to develop a compressor-assisted collective communication framework for large-scale deep learning on GPU-based systems. The two-year project aims to address challenges with the communication overhead of training massive deep learning models by investigating efficient lossy compression techniques for gradient data and...
The National Science Foundation (NSF) awarded a $380,762 Project Grant through its Computer and Information Science and Engineering (CISE) program to Rensselaer Polytechnic Institute (RPI) on October 15, 2023. The grant supports a collaborative research project titled "Graph Neural Networks for Heterophilous Data: Advancing the Theory, Models, and Applications". The project aims to enhance the capability of graph neural networks (GNNs) to effectively model and analyze heterophilous...
This $330,000 Project Grant award from the National Science Foundation's Division of Information and Intelligent Systems (CFDA 47.070 - Computer and Information Science and Engineering) supports collaborative research at the Massachusetts Institute of Technology (MIT) to accelerate the execution of large graph problems on large, distributed computing systems. The project aims to develop new algorithms, software frameworks, and specialized hardware to enable more efficient processing of graph...
This Project Grant award from the National Science Foundation's (NSF) Computer and Information Science and Engineering (CISE) program (CFDA 47.070) provides $160,338 to the Illinois Institute of Technology (IIT) to develop new methods for analyzing, generating, and optimizing graph-structured data. The 3-year project aims to advance graph neural network models and their applications in areas such as social network analysis, molecular design, and physical systems modeling. Key research thrusts...
This National Science Foundation (NSF) Project Grant award, under the Engineering program (CFDA 47.041), provides $439,539 to Rensselaer Polytechnic Institute (RPI) to research computationally efficient learning using graph neural networks (GNNs) with theoretical guarantees. The project aims to establish the theoretical foundations and develop practical algorithms to enhance the efficiency and reliability of GNNs across various engineering applications. Key objectives include analyzing how 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,...