Project Grant 2533631
- This Project Grant award of $120,000 from the National Science Foundation's (NSF) Computer and Information Science and Engineering (CISE) Federal Grant Program supports research to advance the mathematical foundations of generative artificial intelligence (AI) models, particularly diffusion models. The award enables researchers at the University of Missouri System to develop new theoretical tools to elucidate how flow-based generative models produce novel outputs and extend these models to...
- This federal Project Grant award of $599,091, provided by the National Science Foundation's (NSF) Computer and Information Science and Engineering (CISE) program (CFDA 47.070), supports research by the University of Virginia (UVA) to develop techniques that enable modern artificial intelligence (AI) systems to be trained using fewer computing resources. The RINAS project will research methods to address data input/output (I/O) bottlenecks in large-scale deep learning, integrating with popular AI...
- This $300,000 federal Project Grant award was provided by the National Science Foundation's (NSF) Computer and Information Science and Engineering (CISE) program (CFDA 47.070) to the University of Maryland, College Park. The project aims to develop physics-guided generative artificial intelligence models to better understand and predict complex physical processes like pollution transport, virus spread, and wildfire evolution. By integrating physical equations with generative machine learning...
- The National Science Foundation (NSF) Computer and Information Science and Engineering (CISE) Federal Grant Program has awarded $1,000,000.00 to the University of Virginia (UVA) for the project "Generative Imaging Models for Verifying and Explaining Machine Learning Systems in Healthcare". This Project Grant, awarded on September 15, 2025, aims to develop robust methods for ensuring the trustworthiness of deep learning models in healthcare applications. The project will focus on...
- This National Science Foundation (NSF) Computer and Information Science and Engineering (CISE) Federal Grant Program (CFDA 47.070) Project Grant award of $200,000 provides funding to Virginia Polytechnic Institute & State University (Virginia Tech) from September 1, 2024 to August 31, 2027. The project aims to create a more automated and generic framework, along with effective tools, to distill fundamental knowledge of feature spaces and build AI-ready feature spaces using deep generative...
- This federal Project Grant award, valued at $450,000.00 and awarded by the National Science Foundation's (NSF) Computer and Information Science and Engineering (CISE) program (CFDA 47.070), aims to address fundamental computational and statistical limitations of generative artificial intelligence (AI) methods. The key goals of the project are: Determine the capabilities and limitations of generative AI algorithms in terms of the types of probability distributions they can and cannot generate....
- The National Science Foundation (NSF) awarded a $329,183 Project Grant to the College of William & Mary under the Computer and Information Science and Engineering (CFDA 47.070) program. The grant, awarded on October 1, 2023, will fund the development of a framework and methodology to enable researchers and software engineers to better interpret the behavior of AI-powered developer tools that leverage neural language models for source code. The project aims to generate global and local...
- The National Science Foundation's (NSF) Computer and Information Science and Engineering (CISE) Federal Grant Program awarded a $500,000 Project Grant to the Regents of the University of California at Riverside (CFDA 47.070) effective June 15, 2025, with a completion date of May 31, 2028. The grant supports research to understand and mitigate security vulnerabilities in machine learning models that may arise from exploiting unused model parameters. The project aims to empirically study the...
- The National Science Foundation (NSF) Computer and Information Science and Engineering (CISE) Federal Grant Program (CFDA 47.070) has awarded the University of Massachusetts Amherst a $474,083 Project Grant for the period of July 1, 2025 to June 30, 2030. This grant aims to develop embodied generalist AI agents capable of perceiving, reasoning, and interacting effectively with both the physical world and other agents in dynamic, evolving environments. The key technical objectives include: (1)...
- The University of Virginia (UVA) received a $600,000 Project Grant from the National Science Foundation's (NSF) Computer and Information Science and Engineering (CISE) Program (CFDA 47.070) to advance federated graph machine learning (FGML) techniques. The project aims to 1) address data heterogeneity challenges in FGML, 2) develop novel algorithms to tackle label deficiency issues, and 3) strengthen data privacy protection for node attributes and graph structures. The research will produce...
The National Science Foundation (NSF) Computer and Information Science and Engineering (CISE) Federal Grant Program awarded $600,000.00 to the Rector & Visitors of the University of Virginia (CFDA #47.070) for the project "RI:SMALL:CONSTRAINED GENERATIVE MODELS FOR SCIENTIFIC AND ENGINEERING APPLICATIONS". This 3-year project, with an award date of October 1, 2025 and a completion date of September 30, 2028, aims to develop AI tools that integrate physical principles and safety constraints directly into the generative modeling process. The goal is to enable the reliable generation of designs that respect physical laws and safety limitations, reducing costly trial-and-error experimentation and accelerating the development of new materials, devices, and processes across scientific and engineering domains. The project advances machine learning by introducing a new class of training-free, constraint-aware diffusion models that combine differentiable optimization techniques with generative modeling.
Mod # | Description | ReasonForModification | Federal Obligation | Date |
|---|---|---|---|---|
| Not listed | $600.0k | 7/30/25 |