Project Grant 2541023
- Federal Grant Award Summary The National Science Foundation's Division of Information and Intelligent Systems awarded a five-year CAREER grant totaling $405,207 to the University of Minnesota, effective May 1, 2026 through April 30, 2031, under the Computer and Information Science and Engineering (CISE) program (CFDA 47.070). This project grant funds research and development of automated software testing techniques designed to enhance software quality and development efficiency. The primary...
- Federal Grant Award Summary The Regents of the University of Minnesota received a $220,000 Project Grant award from the National Science Foundation's Division of Information and Intelligent Systems (Computer and Information Science and Engineering program, CFDA 47.070) effective August 1, 2025, through July 31, 2027. This collaborative research initiative will develop generative artificial intelligence (AI) methodologies and computational tools designed to accelerate drug discovery and...
- Federal Project Grant Award Summary The University of Chicago received a $342,196 CAREER Project Grant from the National Science Foundation's Division of Computing and Communication Foundations under the Computer and Information Science and Engineering program (CFDA 47.070), awarded August 1, 2026, with a completion date of July 31, 2031. The project develops an innovative adaptive experimental design framework that integrates representation learning with active data acquisition strategies to...
- Federal Project Grant Award Summary The National Science Foundation's Division of Mathematical Sciences awarded $296,023 to the Regents of the University of Minnesota under the Mathematical and Physical Sciences program (CFDA 47.049) effective August 1, 2025, through July 31, 2030. This CAREER award supports fundamental research establishing the mathematical foundations of two critical generative artificial intelligence (AI) models: score-based generative models and transformer-based...
- Federal Grant Award Summary The National Science Foundation (NSF) Division of Mathematical Sciences awarded $255,237 to the University of Minnesota under the Mathematical and Physical Sciences (CFDA 47.049) program for a collaborative research project running from September 1, 2025, through August 31, 2028. This project focuses on developing acceleration and preconditioning methods to optimize deep learning (artificial intelligence) model training processes. The research leverages numerical...
- Federal Grant Award Summary This NSF CAREER award, totaling $560,000 and administered through the Engineering program (CFDA 47.041), funds a five-year project (April 1, 2026 – March 31, 2031) at Rochester Institute of Technology to develop adaptive machine learning systems capable of continuous learning without catastrophic forgetting. The primary deliverables include fundamental algorithms and theoretical frameworks for continual learning that leverage Bayesian uncertainty quantification,...
- Federal Grant Award Summary The University of Minnesota received a $393,750 Project Grant from the National Science Foundation's Division of Computer and Network Systems under the Computer and Information Science and Engineering program (CFDA 47.070), awarded October 1, 2025, with completion targeted for September 30, 2027. This collaborative research initiative develops UNIONLABS, a cloud-based federation platform designed to democratize access to heterogeneous wireless testbeds across multiple...
- Federal Project Grant Award Summary The University of Minnesota received a $300,000 Project Grant award from the National Science Foundation's Division of Electrical, Communications and Cyber Systems under the Engineering program (CFDA 47.041) on October 15, 2025, with a completion date of September 30, 2028. This collaborative research initiative will develop diffractive optical neural networks (DONNs)—specialized, ultrathin computing systems composed of engineered nanostructures that...
- Federal Grant Award Summary The University of Arizona received a $477,128 CAREER (Faculty Early Career Development) Project Grant from the National Science Foundation's Division of Information and Intelligent Systems under the Computer and Information Science and Engineering (CISE) program (CFDA 47.070), effective August 1, 2025 through July 31, 2030. The award supports the development of principled and practical interactive machine learning algorithms designed to address critical challenges...
- Federal Grant Award Summary The National Science Foundation (NSF), Division of Information and Intelligent Systems, awarded $413,700 to the Regents of the University of Michigan under the Computer and Information Science and Engineering (CISE) program (CFDA 47.070) on July 1, 2025, for a CAREER project focused on example-enhanced intelligent tutoring systems. The award, which extends through June 30, 2030, supports the development of scalable upskilling and reskilling programs designed to...
The University of Minnesota's Regents received a $393,606 CAREER (Careers) award from the National Science Foundation (NSF) Division of Computing and Communication Foundations under the Computer and Information Science and Engineering (CISE) program (CFDA 47.070). Funded from July 1, 2026, through June 30, 2031, this five-year project will develop novel artificial intelligence (AI) methods and tools to optimize adaptive experimental design across large, structured design spaces. The primary deliverables include: (1) probabilistic surrogate models that predict experimental outcomes from high-dimensional discrete designs using small datasets; (2) uncertainty-aware deep learning models leveraging historical data while producing reliable prediction intervals; and (3) an information-theoretic framework for experiment selection aligned with diverse scientific objectives such as multi-property optimization, feasible region identification, and discovery of diverse high-quality candidates. In addition to research methodology development, the project will deliver workforce development products including new undergraduate and graduate courses, research training opportunities, open-source computational tools, and benchmark problems to advance AI and scientific discovery. These outputs are designed to reduce the cost and resource intensity of experimental research by enabling engineers and scientists to identify promising discoveries with significantly fewer trials than traditional trial-and-error approaches, thereby accelerating scientific discovery and engineering design across materials science, chemistry, and manufacturing sectors.Federal Grant Award Summary
Mod # | Description | ReasonForModification | Federal Obligation | Date |
|---|---|---|---|---|
| Not listed | $393.6k | 5/13/26 |