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 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 $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 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...
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 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...
This two-year, $251,672 project grant from the National Science Foundation's Computer and Information Science and Engineering program (CFDA 47.070) will support research into non-smooth and non-Lipschitz Riemannian optimization methods. The grantee, Rice University, will study algorithmic approaches such as the manifold alternating direction method of multipliers and inertial manifold proximal gradient method to develop new tools for solving important classes of non-convex optimization...
This Project Grant award, provided by the National Science Foundation (NSF) under the Computer and Information Science and Engineering (CFDA 47.070) program, is funding research to advance decentralized learning methods. The key products and services to be delivered include: The research aims to enable next-generation performance in decentralized learning by addressing challenges related to communication efficiency, data heterogeneity, and algorithmic complexity. The three main research...
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 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...
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 meta-learning, fair batch selection, and AI-aware communication networks. The research will focus on three key thrusts: designing scalable Hessian-free bilevel algorithms, developing primal-dual and pessimistic bilevel methods, and solving bilevel problems on nonlinear manifolds. The research outcomes are expected to benefit academia, government labs, and industry in solving large-scale nested optimization problems across domains like information science, signal processing, communications, statistics, and machine learning.