Project Grant 2502084
- 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 Project Grant award from the National Science Foundation (NSF) under the Computer and Information Science and Engineering (CFDA 47.070) program provides $100,000 to the University of Wisconsin System to conduct research on the mathematical foundations of advanced generative AI models. The project aims to characterize the mathematical principles that underpin the effectiveness of frontier AI models, such as large language models, and identify key mathematical quantities driving their...
- This Project Grant award from the National Science Foundation's (NSF) Mathematical and Physical Sciences program (CFDA 47.049) provides funding of $296,023 to establish the mathematical foundations of two key models used in generative artificial intelligence (AI) methodologies. The primary goals are to: Examine the generative capabilities of score-based generative models in high dimensions and understand the predictive capabilities and limitations of transformer-based foundation models for...
- This $225,000 Project Grant award from the National Science Foundation (NSF) Mathematical and Physical Sciences program (CFDA 47.049) supports research to advance the understanding of generative machine learning models and optimal transport algorithms. The principal investigator at Yale University will study the statistical and computational guarantees of rectified flow and diffusion models, explore connections between these models, and develop novel and improved algorithms to enhance the...
- 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: Algorithms for domain adaptation, reliability metrics for trustworthy AI,...
- This $299,889 federal Project Grant award from the National Science Foundation's (NSF) Mathematical and Physical Sciences (CFDA 47.049) program supports research at the University of California, San Diego (UCSD) to develop algorithms for compressing and improving the efficiency of large neural networks used in modern artificial intelligence applications. The key products and services to be delivered include: The research project focuses on developing quantization, pruning, and low-rank...
- 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...
- The National Science Foundation (NSF) Division of Information and Intelligent Systems awarded a $600,000 Project Grant to the University of California, San Diego (UCSD) under the Computer and Information Science and Engineering (CFDA 47.070) program. The 3-year grant, effective July 1, 2023, supports research to develop dynamic neural network architectures that can efficiently enable multimodal perception, including vision, audio, and language processing. The research aims to address...
- This $6,000,000 Cooperative Agreement awarded by the National Science Foundation (NSF) Division of Computing and Communication Foundations supports the development of foundational tools and mathematical theories to advance the state-of-the-art in generative artificial intelligence (AI). The primary goals are to address core algorithmic challenges in building and deploying large AI models, focusing on training algorithms, model accuracy/robustness, and interpretability. The research is divided...
- This $600,000 project grant, awarded by the National Science Foundation (NSF) under the Computer and Information Science and Engineering (CFDA 47.070) program, will support research to develop a new Bayesian diffusion model framework for advanced visual perception and cognition systems. The University of California, San Diego (UCSD) will serve as the primary awardee, with the goal of revisiting the analysis-by-synthesis methodology by integrating generative priors into the learning and inference...
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, conducted by the University of California, San Diego (UCSD), will also train the next generation of researchers at the intersection of mathematics and AI. The award period runs from Aug 1, 2025 to Jul 31, 2028. This project seeks to strengthen the mathematical foundations of AI and help make the technology safer for real-world use by reducing risks, such as unintentionally copying private training data into public outputs.
Mod # | Description | ReasonForModification | Federal Obligation | Date |
|---|---|---|---|---|
| Not listed | $218.8k | 7/29/25 |