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...
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 National Science Foundation Division of Computer and Network Systems awarded a $175,000 Project Grant to the Illinois Institute of Technology under the Computer and Information Science and Engineering federal grant program (CFDA 47.070) to support research towards understanding the robustness of graph neural networks against graph perturbations. The two-year award beginning June 1, 2023 will fund the development of both restricted and stringent black-box graph perturbation attacks on graph...
The National Science Foundation (NSF) awarded a $400,000 Project Grant under the Computer and Information Science and Engineering (CFDA #47.070) program to the University of Illinois for a 4-year collaborative research project on privacy-preserving machine learning on graph-structured data. The project aims to develop innovative, efficient algorithms for training and updating large-scale graph neural network models while preserving the privacy of sensitive graph data across applications in areas...
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...
This $439,539 Project Grant award from the National Science Foundation's Engineering program (CFDA 47.041) aims to advance the development of computationally efficient and trustworthy graph neural network (GNN) models. The key objectives are to systematically analyze the influence of graph topology and network architecture on GNN performance, establish theoretical foundations, and develop practical algorithms to enhance the efficiency and reliability of GNNs across various engineering...
This National Science Foundation (NSF) Project Grant award under the Computer and Information Science and Engineering program (CFDA 47.070) is for $291,261 over 3 years from January 1, 2024 to December 31, 2026. The grant is funding collaborative research by the Illinois Institute of Technology (IIT) to develop efficient graph neural network (GNN) algorithms and computation systems for both static and dynamic graphs. The key products and services to be delivered include: Design of novel,...
This federal Project Grant award from the National Science Foundation's (NSF) Computer and Information Science and Engineering (CISE) program (CFDA 47.070) provides $266,000 to Georgia State University Research Foundation Inc. to develop novel graph neural network (GNN) models that create smaller compressed graphs while preserving the structural information of large graphs. The goal is to enable GNNs to be deployed in more real-world applications by significantly reducing time and memory...
This NSF Computer and Information Science and Engineering (CISE) Federal Grant Award (CFDA 47.070) totaling $160,338 provides funding for a collaborative research project at the Illinois Institute of Technology (IIT) focused on developing new methods for analyzing, generating, and optimizing graph-structured data. The 3-year project aims to advance graph neural network models and their applications across fields such as social network analysis, molecular design, and the study of physical...