Project Grant 2504090
- 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 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...
- 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 $167,000 Project Grant award from the National Science Foundation's (NSF) Computer and Information Science and Engineering (CISE) program supports the development of a comprehensive framework for making Graph Neural Network (GNN) predictions explainable and trustworthy. The research project, led by The Pennsylvania State University, aims to address the critical need for artificial intelligence systems that can provide accurate predictions while also explaining their reasoning in a way...
- 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 to the University of Rochester under the Computer and Information Science and Engineering (CISE) program (CFDA 47.070) to develop a communication reduction method that integrates graph locality enhancement and high-ratio compression through software-hardware co-design. The project aims to address communication bottlenecks in Graph Neural Networks (GNNs) in order to unlock their potential for real-world applications in...
- This Project Grant award from the National Science Foundation's (NSF) Mathematical and Physical Sciences program (CFDA 47.049) provides $187,000.00 to the Massachusetts Institute of Technology (MIT) to develop mathematical foundations for understanding and improving graph neural networks (GNNs), which are widely used machine learning models for analyzing graph-structured data. The project aims to address key theoretical challenges with GNNs, including limited expressivity, suboptimal performance...
- 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 $500,000 National Science Foundation project grant supports research and education activities at the University of Michigan from October 2022 through September 2026 under the Computer and Information Science and Engineering program. The University of Michigan will conduct collaborative research to advance graph neural network theory, models, and applications for heterophilous data. The researchers will develop new graph neural network designs and architectures that perform well across...
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 GNN learning; 2) introduce data augmentation and test-time adaptation strategies to enhance GNN generalization under out-of-distribution conditions; and 3) create unsupervised graph purification techniques to remove adversarial perturbations and design attack detection mechanisms to improve GNN reliability. This collaborative research project, led by the University of Michigan, will comprehensively evaluate the proposed techniques using publicly available datasets and real-world applications, with the goal of enabling more robust AI systems across critical domains such as healthcare, finance, and national security.
Mod # | Description | ReasonForModification | Federal Obligation | Date |
|---|---|---|---|---|
| Not listed | $300.0k | 7/24/25 |