Project Grant 2502083
- 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 $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 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 $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 $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 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 National Science Foundation (NSF) Engineering (CFDA 47.041) project grant award of $687,382 supports research and education focused on the foundations of the next generation of artificial intelligence (AI) for engineering design. The project, titled "CAREER: TOWARDS NEXTGEN-AI: RETHINKING DEEP GENERATIVE MODELS FOR ENGINEERING DESIGN", aims to establish deep generative models (DGMs) designed to handle challenges specific to engineering design across different scales, complexity,...
- This $300,000 federal Project Grant awarded by the National Science Foundation (NSF) under the Computer and Information Science and Engineering (CFDA 47.070) program supports research to develop physics-guided generative artificial intelligence models for inverting chaotic advection-diffusion dynamics. The research aims to enable more accurate source identification from limited observations of complex physical processes like pollution transport, virus spread, and wildfire evolution, which are...
- 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 $400,000 project grant awarded by the National Science Foundation (NSF) under the Computer and Information Science and Engineering (CFDA 47.070) program supports research to develop a systematic framework for visualizing, understanding, and rewriting the learned computations of multimodal generative AI models. The key objectives are to: 1) create new methodologies to visualize the internal mechanisms and hierarchical structures of pre-trained multimodal generative models, 2) explore model...
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) formulate a general-purpose meta framework for extending flow-based models to complex data structures like graphs, point clouds, and sets. The project, which runs from August 1, 2025 to July 31, 2028, is expected 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. The research will also involve training the next generation of researchers at the intersection of mathematics and AI.
Mod # | Description | ReasonForModification | Federal Obligation | Date |
|---|---|---|---|---|
| Not listed | $100.0k | 7/29/25 |