Project Grant 2504088
- 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 $300,000 Project Grant under the Computer and Information Science and Engineering (CISE) program to Michigan State University (MSU) for the "Collaborative Research: III: Medium: Empowering Graph Neural Networks from a Data Perspective" project. The project aims to overcome key limitations of Graph Neural Network (GNN) models by focusing on improving the quality, scalability, and adaptability of the underlying graph data. Specifically, the...
- This federal Project Grant award for $300,000 from the National Science Foundation's (NSF) Computer and Information Science and Engineering (CISE) program aims to empower Graph Neural Networks (GNNs), a powerful class of artificial intelligence models, by addressing key limitations related to data scale, distribution, and quality. The primary objectives are to: 1) develop graph condensation methods to significantly reduce data size while preserving critical information for efficient and accurate...
- This $800,000 Project Grant award from the National Science Foundation (NSF) under the Computer and Information Science and Engineering (CFDA 47.070) program supports research to establish a computational foundation for safe Graph Neural Networks (GNNs). The 3-year project investigates the end-to-end safety of GNNs, which are a family of deep learning models for interrelated, graph-structured data. The research aims to develop new theories, algorithms, and evaluation methods to enable safer...
- 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 $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...
- 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 $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...
- The National Science Foundation awarded a $499,979 project grant to the George Washington University under the Computer and Information Science and Engineering program (CFDA 47.070). The grant will support research towards developing high-performance machine learning techniques on graphs from October 1, 2021 to September 30, 2024. The Computer and Information Science and Engineering program aims to advance computing and informatics research and education. This award will further those goals by...
- The U.S. National Science Foundation (NSF) awarded a $332,925 project grant under the Computer and Information Science and Engineering (CISE) Federal Grant Program to Florida International University (FIU). The project, titled "COLLABORATIVE RESEARCH: III: SMALL: AN INFORMATION-THEORETIC FRAMEWORK FOR EXPLAINABLE AND EXPLANATION-ASSISTED GRAPH LEARNING", will develop a comprehensive framework for making graph neural network (GNN) predictions explainable and trustworthy. The research...
This federal Project Grant award from the National Science Foundation (CFDA 47.070 - Computer and Information Science and Engineering) provides $400,000 to Emory University to conduct collaborative research on empowering graph neural networks (GNNs) from a data-centric perspective. The project aims to address key challenges with GNNs related to data scale, distribution, and quality, which currently limit the widespread real-world application of these powerful AI models. The research will focus on developing methods to make graph data more compact, cleaner, and better aligned with learning objectives, enabling more efficient, accurate, and robust GNN systems across critical domains such as healthcare, finance, and national security. This 4-year project, running from August 2025 to July 2029, will deliver comprehensive technical advancements and evaluations using publicly available datasets and real-world applications.
Mod # | Description | ReasonForModification | Federal Obligation | Date |
|---|---|---|---|---|
| Not listed | $400.0k | 7/24/25 |