Project Grant 2552008
- The National Science Foundation Division of Computing and Communication Foundations awarded Georgia TECH Research Corp $749,999 on September 1, 2026, under the Computer and Information Science and Engineering program (CFDA 47.070) to develop scalable bilevel optimization algorithms and computational tools for machine learning systems. The project addresses computational bottlenecks in bilevel optimization—a mathematical structure where an overarching objective depends on a nested subordinate...
- This Project Grant award from the National Science Foundation's (NSF) Engineering program (CFDA 47.041) provides $250,000 to The Ohio State University to develop an online bilevel optimization framework for accelerated learning in time-varying environments. The primary objectives are to (i) speed up online bilevel algorithms, improve their scalability, and ensure their performance, and (ii) explore two real-world applications to leverage the advantages of online bilevel optimization in solving...
- The National Science Foundation Division of Computing and Communication Foundations awarded The Johns Hopkins University $188,168 on July 1, 2026, under the Computer and Information Science and Engineering program (CFDA 47.070) for collaborative research in large-scale bilevel optimization theory, algorithms, and applications. The award extends through July 31, 2027, with performance at Baltimore, Maryland. The project develops fast and scalable Hessian-free bilevel algorithms with convergence...
- The National Science Foundation (NSF) awarded a three-year, $300,190 Project Grant under the Computer and Information Science and Engineering (CFDA 47.070) program to The Research Foundation for the State University of New York (RF SUNY) to conduct collaborative research on large-scale bilevel optimization. The key objectives are to develop fast and scalable Hessian-free bilevel optimization algorithms, analyze primal-dual and pessimistic bilevel methods, and devise algorithms for solving...
- The National Science Foundation awarded The Ohio State University a $110,335 Project Grant under the Computer and Information Science and Engineering program (CFDA 47.070) to conduct research on deep sparse models from August 1, 2022 to June 30, 2023. Specifically, the university will advance the theoretical understanding of deep convolutional neural networks through analyzing and developing algorithms for multi-layered convolutional sparse models. Researchers will derive provable and...
- The National Science Foundation (NSF) awarded a $250,000 Project Grant under the Engineering program (CFDA 47.041) to the University of California, Davis (UC Davis) for the period of September 1, 2024 to August 31, 2027. The grant supports the development of an online bilevel optimization framework to address modern challenges in signal processing and machine learning, such as multi-task learning, sequential decision making, and robust adversarial training. The research innovations include...
- The National Science Foundation Division of Computing and Communication Foundations awarded $364,815 to the Regents of the University of California, doing business as University of California, Berkeley, on April 15, 2026, under the Computer and Information Science and Engineering program (CFDA 47.070) to establish rigorous theoretical and algorithmic frameworks for nonconvex and nonsmooth optimization problems. The research focuses on efficient computation of local solutions and effective...
- The National Science Foundation Division of Computing and Communication Foundations awarded Oberlin College $239,830 under the Computer and Information Science and Engineering federal grant program (CFDA 47.070) to support research from October 1, 2022 to September 30, 2025. The award will fund the development of a new theory of computational learning adapted to economic environments where participants may strategically manipulate data. Specifically, the research seeks to design algorithms...
- The Ohio State University will receive $220,000 over three years from the National Science Foundation under a Project Grant for "COLLABORATIVE RESEARCH: CCSS: LEARNING TO OPTIMIZE: FROM NEW ALGORITHMS TO NEW THEORY." The funding falls under the NSF Directorate for Engineering's Engineering program (CFDA 47.041), which aims to foster innovation and excellence in engineering research and education. Specifically, the university will conduct collaborative research developing new algorithms...
- This $400,000 federal Project Grant award was provided by the National Science Foundation (NSF) under the Computer and Information Science and Engineering (CISE) program (CFDA 47.070) to The Ohio State University. The grant supports collaborative research to develop a principled and unified mathematical framework for deep learning on low-dimensional data structures. The key objectives are to: 1) Design "white-box" deep neural networks optimized for information gain and representation...
The National Science Foundation Division of Computing and Communication Foundations awarded The Ohio State University $250,000 on September 1, 2026, under the Computer and Information Science and Engineering program (CFDA 47.070) to develop sample-efficient, scalable bilevel optimization algorithms and computational tools for integration into modern machine learning systems. The project addresses computational bottlenecks in bilevel optimization—a mathematical structure where an overarching goal depends on nested subordinate problems, such as aligning large language model policies with human preference optimization. Existing approaches rely on computationally expensive implicit differentiation or preliminary Lagrangian approximations lacking accessible implementation tools. The research team will develop first-order bilevel algorithms that accommodate stochastic objectives, scale to large problem sizes, and integrate seamlessly into machine learning pipelines. Applications target healthcare interventions for children and diabetes patients, agricultural and manufacturing practices to reduce environmental impacts, and safer artificial intelligence algorithms for improved large language model alignment. The project also supports graduate and undergraduate student training in use-inspired research. Performance runs through August 31, 2030, at Columbus, Ohio. The funding mechanism is a project grant.
Mod # | Description | ReasonForModification | Federal Obligation | Date |
|---|---|---|---|---|
| Not listed | $250.0k | 7/20/26 |