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 (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 (NSF) awarded a $290,000 project grant under its Engineering program (CFDA 47.041) to the University of California, Irvine (UC Irvine) for the period of September 1, 2024 to August 31, 2027. The project, titled "COLLABORATIVE RESEARCH: BLOG: A BI-LEVEL OPTIMIZATION FRAMEWORK FOR LEARNING OVER GRAPHS", aims to develop a unified bi-level optimization-based training framework for machine learning over graphs (LOGS) with automatic selection of...
The National Science Foundation Division of Electrical, Communications and Cyber Systems awarded a $224,375 Project Grant to the University of Texas at Austin from September 15, 2021 through August 31, 2024. The grant supports collaborative research to develop computationally efficient algorithms for large-scale bilevel optimization problems under the NSF Engineering program (CFDA 47.041). The Engineering program seeks to improve quality of life and economic strength by fostering innovation...
This Project Grant award of $450,000 from the National Science Foundation's Engineering program (CFDA 47.041) will support research on bi-level optimization for hierarchical machine learning problems. The award to the Regents of the University of Minnesota, conducting the work through their Office of Sponsored Projects Administration, aims to develop new approaches for modeling, analyzing, and innovating on a wide array of emerging machine learning applications using bi-level optimization...
This Project Grant award of $299,549 from the National Science Foundation's (NSF) Division of Computing and Communication Foundations, under the Computer and Information Science and Engineering program (CFDA #47.070), provides funding to William Marsh Rice University to conduct collaborative research on large-scale bilevel optimization problems. The project aims to develop new theory, algorithms, and applications for bilevel optimization, which has important implications for emerging fields like...
The National Science Foundation (NSF) Directorate for Engineering (ENG) awarded a $250,000 Project Grant to The Research Foundation for The State University of New York (RF SUNY) for the period of August 15, 2023 to July 31, 2026. The grant, titled "COLLABORATIVE RESEARCH: DISTRIBUTED BILEVEL OPTIMIZATION IN MULTI-AGENT SYSTEMS," aims to develop new algorithms for solving bilevel optimization problems over multi-agent distributed networks. The project will focus on three main...
The National Science Foundation (NSF) awarded a 3-year, $249,965 Project Grant to William Marsh Rice University under the Engineering federal grant program (CFDA 47.041) to develop new algorithms for solving bilevel optimization problems in multi-agent distributed systems. The project aims to close the gap between existing bilevel optimization research focused on single-agent systems and the growing need to solve such problems in distributed networks, such as those arising in power systems,...
The University of Arizona was awarded a $224,375 project grant from the National Science Foundation Division of Electrical, Communications and Cyber Systems. The grant supports collaborative research on computationally efficient algorithms for large-scale bilevel optimization problems under the NSF Engineering program (CFDA 47.041). The Engineering program aims to improve quality of life and economic strength by fostering innovation in engineering research. This project specifically develops new...
The National Science Foundation (NSF) awarded a $299,998 Project Grant to Oklahoma State University under the NSF Directorate for Engineering (CFDA 47.041) program. This grant supports fundamental research to enhance the efficiency, robustness, and privacy of decentralized machine learning algorithms for processing distributed datasets. The project aims to develop a theoretical framework for efficient and private decentralized Bayesian learning methods that can produce accurate and reliable...