Project Grant 2502283
- 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 $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...
- The National Science Foundation (NSF) Engineering program (CFDA 47.041) has awarded a $100,000 Project Grant to the University of Washington (UW) for the "Collaborative Research: MFAI: Mathematical Frontiers of Generative AI" project. The grant, awarded on September 1, 2025, aims to uncover the mathematical principles that explain the effectiveness of advanced generative AI models, including large language models, and to overcome the limitations of current brute-force approaches. The...
- The National Science Foundation (NSF) awarded a $163,000 Project Grant under the Mathematical and Physical Sciences grant program (CFDA 47.049) to The Trustees of Columbia University in the City of New York. The goal of this 3-year research project is to build mathematical foundations for understanding the behavior and limitations of modern machine learning systems, with a focus on how AI models represent and learn from data. The research aims to develop general principles for how features...
- 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 $500,000 Project Grant was awarded by the National Science Foundation's (NSF) Engineering program (CFDA 47.041) to The Trustees of the University of Pennsylvania, doing business as Clinical Practices of the University of Pennsylvania, from October 1, 2025 to September 30, 2028. The project, titled "MFAI: Mathematical Foundations of Alignment in Generative Artificial Intelligence," aims to address challenges with large pretrained generative AI models, such as replicating biases,...
- 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 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 $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,...
COLLABORATIVE RESEARCH: MFAI: MATHEMATICAL FRONTIERS OF GENERATIVE AI -FRONTIER AI MODELS HAVE PUSHED THE BOUNDARIES OF MACHINE LEARNING AND ARTIFICIAL INTELLIGENCE RESEARCH AND SPARKED TRANSFORMATIVE TECHNOLOGICAL INNOVATION IN MANY US INDUSTRIES. THESE LARGE-SCALE AI MODELS ARE ABLE TO PROCESS AND GENERATE TEXT, IMAGE, AUDIO, AND VIDEO, AND CURRENTLY REQUIRE MASSIVE AMOUNTS OF DATA AND COMPUTING. THIS MATHEMATICAL FOUNDATIONS OF ARTIFICIAL INTELLIGENCE (MFAI) PROJECT AIMS TO UNCOVER THE MATHEMATICAL PRINCIPLES THAT EXPLAIN WHEN AND WHY THESE HIGHLY ADVANCED AI MODELS ARE SO EFFECTIVE, AND TO OVERCOME THE FUNDAMENTAL LIMITS OF BRUTE-FORCE SCALE PRESENTLY EMPLOYED TO SURPASS HUMAN EXPERT INTELLIGENCE IN BENCHMARKS. THE PROJECT WILL ADVANCE THE CAPABILITIES OF AI MODELS TO CONDUCT INFERENCE IN NEW SITUATIONS IN WHICH THERE IS NO TRAINING DATA, AND TO PERFORM COMPLEX REASONING AND PROBLEM-SOLVING TASKS. THIS RESEARCH WILL ENSURE THAT THE US REMAINS THE GLOBAL LEADER IN AI, ADVANCING ECONOMIC PROSPERITY, NATIONAL SECURITY, AND GLOBAL COMPETITIVENESS. THIS PROJECT AIMS TO RIGOROUSLY CHARACTERIZE THE MATHEMATICAL FRONTIERS OF GENERATIVE AI MODELS, INCLUDING STATE-OF-THE-ART LARGE LANGUAGE MODELS (LLMS), BY DEVELOPING NEW THEORETICAL FRAMEWORKS AND MODELING PRINCIPLES ROOTED IN MACHINE LEARNING, PROBABILITY THEORY, VARIATIONAL ANALYSIS, MATHEMATICAL STATISTICS, AND INFORMATION THEORY. THE RESEARCH WILL INVESTIGATE HOW FRONTIER AI MODELS ACHIEVE REMARKABLE PERFORMANCE DESPITE FUNDAMENTAL THEORETICAL BARRIERS AND WILL IDENTIFY THE KEY MATHEMATICAL QUANTITIES THAT DRIVE THEIR GENERALIZATION ABILITIES. THE PROJECT WILL DEVELOP NEW MATHEMATICAL ANALYSES OF DIFFUSION-BASED GENERATIVE AI MODELS, DESIGN NOVEL DATA STRATEGIES FOR AI MODELS USED TOWARDS ZERO-SHOT INFERENCE, AND DISCOVER SCALING LAWS ENABLING MODELS TO ACHIEVE COMPUTE-OPTIMAL ACCURACY TRADEOFFS FOR INFERENCE AND GENERATION. THIS AWARD IS JOINTLY FUNDED BY THE DIRECTORATE FOR MATHEMATICS AND PHYSICAL SCIENCES, DIVISION OF MATHEMATICAL SCIENCES; DIRECTORATE FOR ENGINEERING, DIVISION OF CIVIL, MECHANICAL, & MANUFACTURING INNOVATION, AND DIRECTORATE FOR COMPUTER & INFORMATION SCIENCE & ENGINEERING, DIVISION OF COMPUTING AND COMMUNICATION FOUNDATIONS AND DIVISION OF INFORMATION & INTELLIGENT SYSTEMS. THIS AWARD REFLECTS NSF'S STATUTORY MISSION AND HAS BEEN DEEMED WORTHY OF SUPPORT THROUGH EVALUATION USING THE FOUNDATION'S INTELLECTUAL MERIT AND BROADER IMPACTS REVIEW CRITERIA.- SUBAWARDS ARE NOT PLANNED FOR THIS AWARD.
Mod # | Description | ReasonForModification | Federal Obligation | Date |
|---|---|---|---|---|
| Not listed | $50.0k | 7/29/25 |