This Project Grant award from the National Science Foundation (CFDA 47.049 - Mathematical and Physical Sciences) to Rensselaer Polytechnic Institute (RPI) provides $220,000 to develop new stochastic algorithms for solving minimax-structured nonconvex and nonsmooth optimization problems. The research aims to improve the robustness of deep learning models against adversarial attacks, with applications in areas like game theory, statistics, engineering, and machine learning. Key deliverables...
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 $600,000 Project Grant award from the National Science Foundation's (NSF) Computer and Information Science and Engineering (CISE) program (CFDA 47.070) supports research at North Carolina State University to explore advanced sampling and optimization techniques for decentralized machine learning. The key objectives are to: Enhance the sampling efficiency of interacting nonlinear Markov chains through adaptive spatio-temporal repellency among multiple "self-repellent random walks",...
This $155,783 National Science Foundation project grant under the Mathematical and Physical Sciences program (CFDA 47.049) funds collaborative research at Syracuse University to develop computationally efficient methods for non-smooth and non-convex optimization by exploring sparsity structures in large data sets. The research aims to address critical issues in non-smooth, non-convex optimization arising from sparse modeling of data for applications including machine learning and sparse...
This $293,784 Project Grant award from the National Science Foundation's (NSF) Mathematical and Physical Sciences (CFDA 47.049) program supports fundamental and applied research on fluctuating systems, random environments, and stochastic algorithms. The research aims to improve understanding and exploitation of randomness across diverse settings, including materials science, fluid dynamics, and machine learning. Key areas of focus include stochastic homogenization, stochastic partial...
This $600,000 National Science Foundation project grant supports research at Stony Brook University to develop a suite of novel distributed reinforcement learning algorithms. The grant is funded through the NSF's Computer and Information Science and Engineering program. Specifically, the three-year award will fund research to establish theoretical foundations for designing, analyzing, and applying fully distributed reinforcement learning algorithms over large-scale networks without global...
This National Science Foundation (NSF) Computer and Information Science and Engineering (CISE) (CFDA 47.070) Project Grant award of $108,000 to the Georgia Tech Research Corp, Office of Sponsored Programs, will fund collaborative research to develop a unified framework for analyzing adaptive stochastic optimization methods for machine learning applications. The research aims to produce self-tuning optimization algorithms with rigorous guarantees to reduce wasteful computation required by current...
This Project Grant award from the National Science Foundation's Engineering program (CFDA 47.041) provides $249,999 to develop a novel communication-efficient hierarchical distributed optimization framework that integrates optimization, communication, and machine learning. The key objectives are to: (1) create a general framework for learning-enabled hierarchical distributed optimization algorithms; (2) develop methods for learning-assisted adaptive quantization, communication, and query; (3)...
This $249,999 federal Project Grant award from the National Science Foundation (NSF) Engineering program (CFDA 47.041) supports the development of a novel communication-efficient hierarchical distributed optimization framework that integrates optimization, communication, and machine learning. The core innovation is to sample and learn models of networked agents' behaviors, then use these models to predict agents' responses and enable informed decision-making while minimizing unnecessary...
The National Science Foundation awarded a $250,000 Project Grant to the Texas A&M Engineering Experiment Station to support research titled "Collaborative Research: Consensus and Distributed Optimization in Non-Convex Environments with Applications to Networked Machine Learning." The award is part of the NSF Engineering program (CFDA 47.041), which aims to foster innovation and excellence in engineering research. Under the three-year award concluding in July 2026, the Texas A&M...