Project Grant 2529284
- 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)...
- This Project Grant from the National Science Foundation's Computer and Information Science and Engineering program (CFDA 47.070) provides $600,000 to The Pennsylvania State University from October 1, 2022 to September 30, 2025. The university will develop interpretable machine learning methods based on deep neural networks from a source coding perspective. Researchers will draw an analogy between explaining complex prediction models and transmitting signals with limited channel capacity. The...
- The National Science Foundation (NSF) awarded a 5-year, $119,764 Project Grant under the Computer and Information Science and Engineering (CFDA 47.070) program to the Trustees of the University of Pennsylvania on June 1, 2025. The grant aims to develop formal specifications, verification frameworks, and certified artificial intelligence (AI) systems that can provide trustworthy and explainable reasoning behind model predictions. The project's key objectives are to bridge the gap between formal...
- 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 $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...
- 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...
- 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 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 National Science Foundation (NSF) Computer and Information Science and Engineering (CISE) Federal Grant Award (CFDA 47.070) provides $592,294 to The Pennsylvania State University to develop secure, robust, and end-user driven prediction-aware counterfactual explanations (CFEs) for machine learning (ML) models. The 3-year project aims to address key limitations of current CFE techniques, including the potential for intellectual property theft, inability to handle model updates, and lack of...
- This $450,000 Project Grant was awarded by the National Science Foundation (NSF) under the Engineering program (CFDA 47.041) to The Trustees of the University of Pennsylvania. The grant supports a collaborative U.S.-Swiss research project focused on developing novel theory and methods for effective, informed graph generation at scale. The key objectives of the project are to: (1) build discrete diffusion processes for progressively adding or removing edges from random graphs to reduce the...
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 trustworthy, addressing the need for AI systems that can provide accurate predictions while also explaining their reasoning. The research will establish mathematical foundations for quantifying explainability in graph learning and translate these insights into practical GNN architectures. The project will also train students in interdisciplinary research and develop educational materials bridging theoretical foundations with real-world implementations. The award period runs from October 1, 2025, to September 30, 2028.
Mod # | Description | ReasonForModification | Federal Obligation | Date |
|---|---|---|---|---|
| Not listed | $167.0k | 7/29/25 |