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...
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...
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...
This $600,000 Project Grant award from the National Science Foundation's (NSF) Computer and Information Science and Engineering (CISE) program (CFDA 47.070) supports fundamental research into the mathematical foundations for using graph-structured data in machine learning applications. The key products and services to be delivered under this 3-year award include: Characterizing how the geometry of the underlying latent space affects structural and combinatorial properties of graphs, to enable...
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...
This Project Grant award from the National Science Foundation's (NSF) Division of Mathematical Sciences (CFDA 47.049 - Mathematical and Physical Sciences) supports research to develop novel mathematical models and efficient algorithms for deep learning on large-scale graph-structured data. The $249,999 award, spanning September 2024 to August 2027, aims to produce innovations in areas like graph convolutional networks, graph matching, and graph clustering. The research will involve graduate...
This National Science Foundation (NSF) award under the Computer and Information Science and Engineering (CFDA 47.070) Federal Grant Program provides $598,448 to Cornell University from December 2024 to November 2027 to develop theoretical and algorithmic foundations for online learning and decision-making involving sequential data under unknown stochastic models. The research focuses on advancing statistical inference and learning methodologies, both centralized and distributed, for critical...
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 National Science Foundation (NSF) Engineering (CFDA 47.041) project grant awarded to the University of California, Irvine aims to develop a systematic understanding of unfairness in learning over graphs (LOG) and design efficient, principled algorithms for fair LOG. The $550,000 award, effective from October 1, 2025 to September 30, 2030, will provide transformative advances at the intersection of machine learning, optimization, and network science to mitigate potential bias in learning...
This $150,000 Project Grant award from the National Science Foundation's (NSF) Computer and Information Science and Engineering (CISE) program (CFDA 47.070) supports collaborative research to develop new methods for analyzing, generating, and optimizing graph-structured data. The project aims to create more expressive and efficient graph neural network models, improved generative models for graphs, and apply graph learning techniques to optimization problems and physical systems modeling. The...