Project Grant 2504089
- 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 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...
- 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 National Science Foundation project grant of $300,000 supports research at Michigan State University to advance graph neural network models for heterophilous data under the Computer and Information Science and Engineering program. The award period is from October 1, 2022 to September 30, 2026. The project aims to generalize graph neural networks to work effectively on a wider range of domains characterized by heterophily, or low homophily. It will introduce new graph neural network...
- 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...
- 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...
- 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 $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...
- The National Science Foundation (NSF) awarded a Project Grant totaling $379,999 to Michigan State University under the Federal Grant Program for Mathematical and Physical Sciences (CFDA 47.049) to develop novel machine learning techniques that can effectively learn from limited labeled data. The project aims to address the key limitation of most machine learning methods, which rely on large labeled datasets that are often scarce and expensive to obtain. Specifically, the research will focus on...
- 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...
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 researchers will develop methods to condense large graph datasets while preserving critical information, create data augmentation and adaptation strategies to enhance model generalization, and design techniques to detect and mitigate adversarial attacks on graph data. The project will evaluate these innovations using publicly available datasets and real-world applications, with the goal of enabling more efficient, accurate, and robust AI systems across domains like healthcare, finance, and national security. The award period is from August 1, 2025 to July 31, 2029.
Mod # | Description | ReasonForModification | Federal Obligation | Date |
|---|---|---|---|---|
| Not listed | $300.0k | 7/24/25 |