Project Grant 2529283
- This federal Project Grant award from the National Science Foundation (NSF) Computer and Information Science and Engineering (CISE) program provides $167,000.00 to The Pennsylvania State University (Penn State) for the project "COLLABORATIVE RESEARCH: III: SMALL: AN INFORMATION-THEORETIC FRAMEWORK FOR EXPLAINABLE AND EXPLANATION-ASSISTED GRAPH LEARNING." The project aims to develop a comprehensive framework for making graph neural network (GNN) predictions explainable and...
- This Project Grant award from the National Science Foundation (NSF) Computer and Information Science and Engineering (CISE) Federal Grant Program (CFDA 47.070) provides $400,000 to Emory University to conduct collaborative research on empowering Graph Neural Networks (GNNs) from a data perspective. The key objectives are to: Develop methods to make graph data more compact, cleaner, and better aligned with learning objectives to enable more efficient, accurate, and robust AI systems across...
- This $350,000 Project Grant was awarded by the National Science Foundation's (NSF) Computer and Information Science and Engineering (CISE) program on August 1, 2025. The grant will be used by the University of California, Berkeley to develop scalable algorithms for making artificial intelligence (AI) model predictions more understandable and explainable. This research aims to advance the transparency and trustworthiness of AI systems, which is critical for their safe deployment in applications...
- 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 Project Grant award of $300,000.00 from the National Science Foundation (NSF) Computer and Information Science and Engineering (CISE) Federal Grant Program supports research to empower graph neural networks (GNNs) from a data perspective. The project aims to address key limitations of GNNs, which are a powerful class of AI models used to analyze complex data relationships, by focusing on improving the scalability, distribution robustness, and data quality of graph data. The research tasks...
- This federal Project Grant award from the National Science Foundation (NSF) Computer and Information Science and Engineering (CISE) program provides $342,235 to the University of California, Irvine (UCI) over a period of 5 years starting on October 1, 2025. The goal of this project is to develop novel explainability tools and techniques that enable effective human-AI collaboration during the design and deployment of learning-based network controllers. Specifically, the project aims to create a...
- 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 $300,000 EAGER (Early-concept Grants for Exploratory Research) award from the National Science Foundation's (NSF) Computer and Information Science and Engineering (CISE) program aims to develop a framework called XAISE (eXplainable Artificial Intelligence for Science and Engineering) to enhance the explainability of artificial intelligence (AI) models for scientific and engineering applications. The project seeks to design, develop, and implement XAISE to improve the explainability of...
- This $300,000 Project Grant was awarded by the National Science Foundation (NSF) under the Computer and Information Science and Engineering (CFDA 47.070) federal grant program. The grant will fund collaborative research to empower graph neural networks (GNNs) by addressing key challenges related to data scale, distribution, and quality. The research aims to develop methods for condensing graph data, enhancing GNN generalization under distribution shifts, and creating techniques to detect and...
- The National Science Foundation (NSF) awarded a $582,031 Project Grant under the Computer and Information Science and Engineering program (CFDA 47.070) to the Regents of the University of Michigan, doing business as the University of Michigan. The grant will fund a 5-year research project focused on "Achieving Explainable Artificial Intelligence (AI) Through Human-AI Interaction." The goal of the project is to develop new scientific knowledge and design guidelines for delivering...
This federal Project Grant award from the National Science Foundation's (NSF) Computer and Information Science and Engineering (CISE) program, titled "COLLABORATIVE RESEARCH: III: SMALL: AN INFORMATION-THEORETIC FRAMEWORK FOR EXPLAINABLE AND EXPLANATION-ASSISTED GRAPH LEARNING", provides $332,925 to Florida International University (FIU) over a period of 3 years starting October 1, 2025. The project aims to develop a comprehensive framework for making graph neural network (GNN) predictions explainable and trustworthy, addressing the need for AI systems that can provide accurate predictions while also explaining their reasoning in ways that domain experts can understand and verify. The research will establish a unified framework using information theory concepts to enable explainable GNNs, with two main thrusts: 1) developing mathematical foundations for quantifying explainability in graph learning, and 2) translating these theoretical insights into practical GNN architectures. The project will also train students in this interdisciplinary research combining machine learning, information theory, and practical applications, while developing educational materials to bridge theoretical foundations with real-world implementations.
Mod # | Description | ReasonForModification | Federal Obligation | Date |
|---|---|---|---|---|
| Not listed | $332.9k | 7/29/25 |