Project Grant 2513668
- This $175,000 Project Grant award from the National Science Foundation's (NSF) Mathematical and Physical Sciences program (CFDA 47.049) will fund research to develop new statistical and computational methods to enhance the reliability of data analysis in modern, large-scale datasets, particularly in the era of AI. The key areas of focus include: (1) analyzing the robustness of manifold and deep learning algorithms for high-dimensional, noisy, and nonlinear data; (2) developing statistical theory...
- This federal Project Grant award of $100,000.00, provided by the National Science Foundation (NSF) under the Mathematical and Physical Sciences (CFDA 47.049) program, supports research on advanced probabilistic models and their application to cutting-edge machine learning techniques. The research aims to bring mathematical rigor and develop new methods related to complex systems in areas such as image processing, reinforcement learning, and generative AI. Key focus areas include: 1) extracting...
- This $100,000 Project Grant awarded by the National Science Foundation (NSF) Engineering program (CFDA 47.041) supports research to elucidate the fundamental mechanisms underlying flow-based generative AI models, such as diffusion models, and extend their capabilities to handle complex data types. The research aims to: (1) understand why trained flow models often generalize better than theoretically expected, using tools from geometry, ODE, manifold learning, and deep learning theory; and (2)...
- This $140,000 federal Project Grant award from the National Science Foundation's (NSF) Mathematical and Physical Sciences (CFDA 47.049) program will develop a next-generation statistical framework to improve the reliability and reproducibility of data science (DS) and artificial intelligence (AI) methods. The project, titled "Collaborative Research: Performance Guaranteed Statistical Learning with Multiple Classes of Models (Guided by PCS)," aims to advance a framework called...
- This $245,190 Project Grant awarded by the National Science Foundation's (NSF) Mathematical and Physical Sciences program (CFDA 47.049) aims to advance the integration of modern machine learning tools, such as deep learning and Bayesian additive regression trees, into statistical modeling frameworks. The research program has two key objectives: Developing a novel Bayesian inferential framework for "generative models" - statistical models where data is viewed as stochastic outputs of...
- This Project Grant award, valued at $325,000.00, was provided by the National Science Foundation's Social, Behavioral, and Economic Sciences (CFDA 47.075) program to the University of Washington. The project develops powerful new tools for understanding complex data, leveraging cutting-edge artificial intelligence (AI) techniques to help data analysts across diverse fields make informed, automated decisions. The research introduces novel approaches to analyze messy, heterogeneous, and large...
- This $218,771 Project Grant award from the National Science Foundation's (NSF) Computer and Information Science and Engineering (CISE) program supports research to develop new theoretical tools to enhance flow-based generative artificial intelligence (AI) models. The research aims to elucidate how these models, including diffusion models, produce novel outputs and extend their capabilities to handle complex data types beyond the Euclidean setting, such as graphs and point clouds. The project,...
- This Project Grant award from the National Science Foundation (NSF) under the Mathematical and Physical Sciences federal grant program (CFDA 47.049) provides $118,132 to the University of Illinois to conduct research on statistical aspects of diffusion models, an emerging class of generative modeling techniques. The research project aims to (1) determine the statistical limits of diffusion models, (2) optimize the query complexity for sampling in diffusion models, and (3) understand the...
- This $599,411 Project Grant award from the National Science Foundation's (NSF) Computer and Information Science and Engineering (CISE) program (CFDA 47.070) supports the development of a "Trustworthy Toolbox for Double-Correct Predictive Modeling in Sciences." The project aims to create advanced artificial intelligence (AI) and machine learning (ML) models that can make accurate predictions while also providing transparent, scientifically-grounded rationales for their outputs. This...
- This $300,000 Project Grant was awarded on September 1, 2025 by the National Science Foundation's (NSF) Mathematical and Physical Sciences program (CFDA 47.049). The grant will fund research at Carnegie Mellon University to develop mathematically sound approaches for sampling and generative modeling in high-dimensional problems, which is critical for advancing machine learning and artificial intelligence (AI) techniques. The research aims to create efficient sampling methods with rigorous...
This $175,000 federal Project Grant award from the National Science Foundation's (NSF) Mathematical and Physical Sciences program (CFDA 47.049) supports the development of a unified generative prediction and inference framework using diffusion processes, normalizing flows, and transfer learning to model joint distributions of tabular and unstructured data. The key products and services delivered under this award include: This research aims to accelerate scientific discovery, strengthen the technological workforce, and support informed decision-making across health, commerce, and security applications. No subawards are planned for this award, which has an ultimate completion date of July 31, 2028.
Mod # | Description | ReasonForModification | Federal Obligation | Date |
|---|---|---|---|---|
| Not listed | $175.0k | 8/14/25 |