Project Grant 2610649
- This Project Grant award of $400,000.00 from the National Science Foundation's (NSF) Computer and Information Science and Engineering (CISE) program (CFDA 47.070) supports research to empower Graph Neural Networks (GNNs) from a data-centric perspective. The project aims to address key challenges facing widespread GNN adoption, including scalability, generalization, and robustness, by focusing on improving the underlying graph data. The research tasks include developing graph condensation methods...
- This National Science Foundation (NSF) Engineering (CFDA 47.041) Project Grant award to Rensselaer Polytechnic Institute (RPI) provides $439,539 over 3 years (2025-2027) to support research on computationally efficient graph neural networks (GNNs) with theoretical guarantees. The key objectives are to systematically analyze how graph topology and network architecture influence GNN performance, optimize computational and memory resources through techniques like graph data aggregation and...
- This $300,000 Project Grant award, funded by the National Science Foundation (NSF) under the Computer and Information Science and Engineering (CISE) program (CFDA 47.070), aims to empower graph neural networks (GNNs) by focusing on improving the underlying graph data. The key research objectives are to: Develop graph condensation methods to significantly reduce data size while preserving critical information, addressing GNN scalability challenges. Investigate distribution shifts related to graph...
- This Project Grant award from the National Science Foundation's (NSF) Computer and Information Science and Engineering (CISE) Federal Grant Program (CFDA 47.070) provides $300,000 in funding to the University of Michigan to conduct collaborative research aimed at empowering Graph Neural Networks (GNNs) from a data perspective. The project seeks to address core GNN challenges related to data scale, distribution, and quality. Key objectives include: 1) developing graph condensation methods to...
- 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 Project Grant award of $423,215 from the National Science Foundation (NSF) under the Computer and Information Science and Engineering (CFDA 47.070) program seeks to accelerate the execution of large graph problems on distributed computing systems. The research aims to develop new algorithms, software frameworks, and heterogeneous hardware to enable more efficient processing of graph computations that arise in areas like computational biology, social network analysis, and software...
- This federal Project Grant award of $187,000.00 from the National Science Foundation's (NSF) Mathematical and Physical Sciences program (CFDA 47.049) aims to develop mathematical foundations for understanding and improving graph neural networks (GNNs), which are widely used machine learning models for data with graph structures. The key products or services to be delivered through this 3-year award (July 1, 2025 to June 30, 2028) include: Developing GNN architectures for solving quadratic...
- This $195,605 Project Grant award from the National Science Foundation's (NSF) Computer and Information Science and Engineering (CISE) Federal Grant Program (CFDA 47.070) aims to develop innovative frameworks for constructing advanced data compression and communication algorithms. The University of Texas at Austin is the awardee and will integrate insights from information theory, generative models, and deep learning to establish new methodologies that can drive the discovery of more efficient...
- 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 National Science Foundation (NSF) Computer and Information Science and Engineering (CISE) Project Grant award, funded under the American Rescue Plan Act of 2021, provides $130,155 to Rensselaer Polytechnic Institute (RPI) for a project titled "CRII:III:TOWARDS ADVANCED FILTERING AND POOLING OPERATIONS FOR GRAPH NEURAL NETWORKS." The aim is to conduct theoretical analysis and develop innovative algorithms to address limitations in the efficacy and efficiency of graph neural...
This federal Project Grant award, with a total funding amount of $264,299, was provided by the National Science Foundation's Computer and Information Science and Engineering (CISE) program (CFDA 47.070). The grant is being awarded to William Marsh Rice University in Houston, TX to support a research project titled "Reimagining Communication Bottlenecks in GNN Acceleration Through Collaborative Locality Enhancement and Compression Co-Design." The project aims to develop a revolutionary communication reduction method that integrates graph locality enhancement and high-ratio compression through software-hardware co-design to address computational inefficiencies in graph neural networks (GNNs). This research has the potential to significantly impact fields such as medicine, public infrastructure, and economic development by unlocking the immense potential of GNNs. The award period is from January 1, 2026 to September 30, 2026. The research will also enrich the educational experience of undergraduate and graduate students at the University of Rochester and Indiana University through AI and systems-related courses and outreach activities.
Mod # | Description | ReasonForModification | Federal Obligation | Date |
|---|---|---|---|---|
| Not listed | $264.3k | 12/30/25 |