Project Grant 2610168
- Federal Grant Award Summary The University of Michigan received a $300,000 Project Grant award from the National Science Foundation's Division of Information and Intelligent Systems under the Computer and Information Science and Engineering program (CFDA 47.070), effective August 1, 2025, through July 31, 2029. This collaborative research initiative focuses on enhancing Graph Neural Networks (GNNs) through data-centric improvements rather than model refinement alone. The project delivers three...
- Federal Project Grant Award Summary The University of Michigan, Office of Research and Sponsored Projects, received a $150,000 Project Grant from the National Science Foundation's Division of Mathematical Sciences under the Mathematical and Physical Sciences program (CFDA 47.049), awarded on August 15, 2025, with completion targeted by July 31, 2028. The award supports fundamental research on robust data-driven decision-making systems that integrate human-AI alignment with algorithmic...
- Federal Project Grant Award Summary Michigan State University received a $379,999 Project Grant from the National Science Foundation (NSF) Division of Mathematical Sciences under the Mathematical and Physical Sciences program (CFDA 47.049), awarded September 1, 2025, with a completion date of August 31, 2028. The award supports development of novel graph-based semi-supervised machine learning techniques designed to enable effective learning from datasets with limited labeled data. The project...
- Federal Grant Award Summary Michigan State University received a $300,000 Project Grant from the National Science Foundation (NSF) Division of Information and Intelligent Systems under the Computer and Information Science and Engineering (CISE) program (CFDA 47.070), effective August 1, 2025, through July 31, 2029. This collaborative research initiative addresses fundamental limitations in Graph Neural Networks (GNNs) by focusing on data-centric improvements rather than model refinement alone....
- Federal Project Grant Award Summary The National Science Foundation's Division of Mathematical Sciences awarded a $200,000 Project Grant to the University of Michigan, effective September 1, 2025, through August 31, 2028, under the Mathematical and Physical Sciences program (CFDA 47.049). This award funds fundamental research on Wasserstein Partial Differential Equations (PDEs) and their applications to optimization and machine learning. The investigator will conduct rigorous theoretical...
- Federal Grant Award Summary The University of Michigan received a $155,000 Project Grant from the National Science Foundation's Division of Information and Intelligent Systems under the Computer and Information Science and Engineering program (CFDA 47.070), effective October 1, 2025 through September 30, 2027. This collaborative research initiative develops generative artificial intelligence (GenAI) methods to enhance machine learning-based security classifiers by addressing data challenges in...
- Federal Grant Award Summary The National Science Foundation (NSF) Division of Social, Behavioral and Economic Science awarded $500,000 under the Social, Behavioral, and Economic Sciences program (CFDA 47.075) to the University of Michigan for a project grant beginning September 15, 2025, with completion targeted for August 31, 2027. The project develops artificial intelligence technology, specifically leveraging large language models (LLMs), to extract and analyze key information from video...
- Federal Grant Award Summary The University of Michigan received a $102,399 Project Grant award from the National Science Foundation's Division of Information and Intelligent Systems under the Computer and Information Science and Engineering (CISE) program (CFDA 47.070), effective August 15, 2025, with a completion date of July 31, 2027. This planning grant supports the development of an AI-ready testbed for studying municipal government service delivery at scale. The project will deliver four...
- Federal Grant Award Summary Michigan State University received a $180,000 Project Grant from the National Science Foundation (NSF) under the Mathematical and Physical Sciences program (CFDA 47.049), awarded August 15, 2025, with completion targeted for July 31, 2028. The project delivers research and educational products focused on establishing statistical frameworks for robust adversarial training in neural networks. The primary deliverables include: (1) theoretical foundations for...
- Federal Project Grant Award Summary The University of Michigan received a $329,020 CAREER award from the National Science Foundation (NSF), Division of Information and Intelligent Systems, under the Computer and Information Science and Engineering (CISE) program (CFDA 47.070), effective June 15, 2025, through May 31, 2030. This project grant funds the development of novel analytical methodologies and computational tools designed to enable causal inference and robust statistical modeling on...
The University of Michigan received a $325,000 Project Grant from the National Science Foundation (NSF), Division of Mathematical Sciences, under the Mathematical and Physical Sciences (CFDA 47.049) program, effective July 1, 2026, through June 30, 2029. This award supports the development of machine learning and statistical methods for analyzing heterogeneous network data—a critical capability for applications spanning neuroscience, social science, economics, and biomedicine. The research deliverables include AI-driven methodologies for prediction and inference in single and multiple network settings, with emphasis on interpretable approaches that quantify uncertainty and provide theoretical performance guarantees. Specific technical products include tools to quantify predictive contributions of node covariates and network structure within flexible machine learning models; methods to detect and test for latent subgroups in network-linked data; and methodologies to test differences between collections of networks and estimate shared, group-specific, and individual-level network structures across populations. Practical applications include comparative analyses of brain connectivity networks between patient and control groups at both global and localized levels. The project integrates network machine learning, uncertainty quantification, and scalable algorithm design with rigorous theoretical guarantees, while also contributing to graduate student training in artificial intelligence, machine learning, and network data science.Summary of Federal Project Grant Award
Mod # | Description | ReasonForModification | Federal Obligation | Date |
|---|---|---|---|---|
| Not listed | $325.0k | 5/13/26 |