Project Grant 2608887
- 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...
- 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 National Science Foundation (NSF) project grant under the Computer and Information Science and Engineering (CISE) program (CFDA 47.070) was awarded to Carnegie Mellon University in the amount of $600,000 on August 15, 2024. The project will build mathematical foundations for using machine learning methods, specifically neural networks, to improve the process of solving partial differential equations (PDEs) and leverage PDEs as a tool for generative modeling. The research will explore issues...
- 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 CAREER award from the National Science Foundation (NSF) Engineering program (CFDA 47.041) provides $687,382 in funding to the Massachusetts Institute of Technology (MIT) to support research and education focused on the foundations of the next generation of artificial intelligence (AI) for engineering design. The project aims to establish deep generative models (DGMs) that can effectively address challenges specific to engineering design at different scales, complexity, and disciplinarity....
- This $298,450 National Science Foundation project grant supports research to quantify the error landscape of deep neural networks. Funded under the Computer and Information Science and Engineering program (CFDA 47.070), the awardee New York University will employ statistical mechanics methods to characterize the basins of attraction in high-dimensional parameter spaces of deep learning models. The university will measure basin volume distributions and flatness as a function of network parameters...
- This $400,000 Project Grant award was provided by the National Science Foundation (NSF) under the Computer and Information Science and Engineering (CFDA 47.070) Federal Grant Program. The project aims to develop a systematic framework for visualizing, understanding, and rewriting the learned computations of multimodal generative AI models, in order to increase the accountable and safe use of these advanced AI systems and mitigate potential harms. The key research thrusts involve: 1) new...
- This Project Grant award from the National Science Foundation's (NSF) Computer and Information Science and Engineering (CISE) program (CFDA 47.070) provides $309,407 to Virginia Polytechnic Institute & State University (Virginia Tech) to develop a framework for ensuring the safety and trustworthy deployment of generative artificial intelligence (AI) foundation models, particularly large language models. The project will pursue three key tasks: 1) Conduct in-depth analysis to identify root...
- This two-year, $198,375 Project Grant from the National Science Foundation's Division of Information and Intelligent Systems will support the development of scalable and robust computational methods to advance artificial intelligence through innovations in deep learning with big imbalanced data. Funded under the Computer and Information Science and Engineering program, the grant aims to address data imbalance challenges that occur when training data for AI models does not adequately represent...
- Federal Grant Award Summary The National Science Foundation's Division of Mathematical Sciences awarded a five-year CAREER grant of $243,000 to Columbia University beginning September 1, 2026, under the Mathematical and Physical Sciences program (CFDA 47.049). This project develops a statistical framework for evaluating uncertainty quantification and principled design of generative artificial intelligence (AI) systems. The research pursues three primary thrusts: (1) measuring overall fidelity of...
STABLE FORMULATIONS AND NUMERICAL DISCRETIZATIONS OF LOSS FUNCTIONS FOR GENERATIVE AI WITH APPLICATIONS TO MODEL REDUCTION -ARTIFICIAL INTELLIGENCE IS INCREASINGLY USED TO MODEL COMPLEX SCIENTIFIC AND ENGINEERING SYSTEMS. MAINTAINING LEADERSHIP IN THIS AREA OVER THE LONG TERM DEPENDS NOT ONLY ON LARGER MODELS AND MORE DATA BUT ALSO ON MATHEMATICAL METHODS TO ENHANCE TRAINING AND TO MAKE TRAINING MORE STABLE, REPRODUCIBLE, AND MATHEMATICALLY WELL-FOUNDED. THIS PROJECT WILL DEVELOP MATHEMATICAL PRINCIPLES FOR DESIGNING TRAINING OBJECTIVES AND METHODS FOR GENERATIVE ARTIFICIAL INTELLIGENCE THAT REMAIN STABLE UNDER REALISTIC TRAINING CONDITIONS, SUCH AS FINITE DATA, RATHER THAN PRODUCING MISLEADING UPDATES OR FRAGILE MODELS. THIS PROJECT DIRECTLY ADVANCES ARTIFICIAL INTELLIGENCE AS AN AREA OF FEDERAL STRATEGIC INTEREST BY STRENGTHENING THE FOUNDATIONS NEEDED FOR RELIABLE ARTIFICIAL INTELLIGENCE MODELS IN SCIENTIFIC AND ENGINEERING APPLICATIONS. MORE BROADLY, THIS PROJECT WILL STRENGTHEN THE SCIENTIFIC AND INDUSTRIAL ARTIFICIAL INTELLIGENCE ECOSYSTEM IN THE UNITED STATES BY DEVELOPING RELIABLE METHODS FOR DISCOVERY, DESIGN, AND DECISION-MAKING IN COMPLEX SYSTEMS. THE PROJECT WILL ALSO SUPPORT EDUCATION AND WORKFORCE DEVELOPMENT THROUGH GRADUATE STUDENT TRAINING, INTEGRATION OF PROJECT IDEAS INTO COURSES, OPEN SOURCE SOFTWARE, AND PUBLIC BENCHMARK PROBLEMS. THE PROJECT WILL DEVELOP STABLE FORMULATIONS AND NUMERICAL DISCRETIZATIONS OF LOSS FUNCTIONS FOR GENERATIVE ARTIFICIAL INTELLIGENCE MODELS OF TIME-DEPENDENT STOCHASTIC PROCESSES. THESE MODELS OFTEN USE TRAINING OBJECTIVES INVOLVING TIME AND SPACE DERIVATIVES, BUT CURRENT PRACTICE COMMONLY ESTIMATES SUCH OBJECTIVES FROM SAMPLES IN A BLACK BOX MANNER, WHICH CAN INTRODUCE SYSTEMATIC ERRORS, POOR CONDITIONING, AND UNSTABLE TRAINING. THE PROJECT WILL SHOW THAT LOSS FUNCTIONS FOR CERTAIN GENERATIVE MODELS CAN BE INTERPRETED AS VARIATIONAL FORMULATIONS OF PARTIAL DIFFERENTIAL EQUATIONS. THIS CONNECTION WILL MAKE IT POSSIBLE TO TRANSFER CONCEPTS SUCH AS STABILITY, COERCIVITY, STRUCTURE PRESERVATION, AND CONSISTENT DISCRETIZATION INTO THE DESIGN OF EMPIRICAL LOSS FUNCTIONS FOR DATA-DRIVEN GENERATIVE MODELING. THE WORK WILL ESTABLISH RIGOROUS CORRESPONDENCES BETWEEN CONTINUOUS LOSS FUNCTIONS AND PARTIAL DIFFERENTIAL EQUATION FORMULATIONS, DERIVE WELL-CONDITIONED DISCRETE LOSS FUNCTIONS THAT REMAIN STABLE WHEN ONLY DATA SAMPLES ARE AVAILABLE, AND DEVELOP DISCRETIZATION STRATEGIES THAT ALSO PROVIDE ALGORITHMIC BENEFITS SUCH AS PARALLEL TRAINING ACROSS TIME. THE RESULTING METHODS WILL BE APPLIED TO REDUCED AND SURROGATE MODELING OF STOCHASTIC AND CHAOTIC SYSTEMS, TO DEMONSTRATE MORE STABLE, RELIABLE, AND EFFICIENT GENERATIVE REDUCED MODELS. 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 | ($397k) | 6/16/26 | ||
| Not listed | $397.5k | 5/27/26 |