Project Grant 2348624
- 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 $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 $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 federal Project Grant awarded by the National Science Foundation (NSF) under the Computer and Information Science and Engineering (CISE) program provides $594,753 to Cornell University to develop new methods for controlling generative artificial intelligence (AI) systems that produce text and images. The research aims to improve the reliability and safety of these AI technologies, especially in sensitive applications like healthcare, customer service, and education. The project will...
- 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 $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...
- The University of Iowa was awarded a $299,997 Project Grant from the National Science Foundation's Computer and Information Science and Engineering (CISE) program (CFDA 47.070) to conduct research on "Reverse Discrete Time Diffusions: Transforming Generative AI." The project aims to develop a fundamental theory for directly reversing certain stochastic diffusion processes that underpin generative AI algorithms, which are used to create synthetic data like images, videos, and text. This...
- This $400,000 Project Grant awarded by the National Science Foundation (NSF) under the Computer and Information Science and Engineering (CFDA 47.070) program supports the development of a new framework and tools for advancing data-centric artificial intelligence (AI) through generative approaches to feature space reconstruction. The project aims to transform the traditional way of constructing feature spaces by using deep generative learning instead of manual or classical discrete search...
- 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 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 $600,000 Project Grant award from the National Science Foundation (NSF) Computer and Information Science and Engineering (CISE) program supports research by the University of Illinois to develop a theoretical control framework for understanding and improving diffusion-based generative machine learning models. The project aims to establish connections between generative modeling, optimal control theory, and partial/stochastic differential equations. Key technical objectives include enhancing the controllability, expressiveness, computational efficiency, and robustness of diffusion models used for applications like image generation. The award also incorporates undergraduate research participation and educational activities addressing the ethical and societal implications of generative machine learning. This work is expected to be completed over a 3-year period from March 2024 to February 2027. No sub-awards are planned under this grant.
Mod # | Description | ReasonForModification | Federal Obligation | Date |
|---|---|---|---|---|
| Not listed | $600.0k | 1/2/24 |