This $199,996 project grant awarded by the National Science Foundation (NSF) Engineering program (CFDA 47.041) aims to develop a principled approach to the systematic design of efficient iterative algorithms for a wide variety of data-driven applications. The project at Miami University will leverage tools from both optimization and control theory, including techniques like interpolation, Lyapunov stability, and robust control synthesis, to enable the deployment of specialized and efficient...
This Project Grant award from the National Science Foundation (NSF) Computer and Information Science and Engineering (CISE) Federal Grant Program, with a total funding of $263,764, supports fundamental research on optimization techniques that enable faster and more accurate algorithms for modern computing applications, including artificial intelligence and scientific computing. The research focuses on three main thrusts: (1) understanding the complexity of the widely used interior-point method...
This three-year $300,000 Project Grant from the National Science Foundation's Division of Electrical, Communications and Cyber Systems, under the Engineering (47.041) federal grant program, funds research at New York University to advance the mathematical foundations and develop new tools for real-time distributed optimization-based control of large-scale nonlinear uncertain systems. Specifically, the award supports three research tasks: 1) synthesizing distributed optimization algorithms robust...
This $300,000 National Science Foundation project grant supports research into distributed optimization-based control of large-scale nonlinear systems with uncertainties from 2022-2025. Funded under the NSF Engineering program (CFDA 47.041), the award to the University of California, San Diego will advance mathematical foundations for distributed optimization algorithms robust to uncertainties. Researchers will design tracking controllers for local systems to follow optimization-derived...
The University of Minnesota was awarded a three-year, $335,337 Project Grant from the National Science Foundation's Engineering program (CFDA 47.041) to develop mechanics-based algorithms for sampling, control, and learning in non-convex domains. The grant will support research from September 2021 through August 2024 to improve techniques for modeling and optimizing complex systems with non-smooth or discontinuous behaviors. As the NSF's Engineering directorate seeks to advance innovation and...
The National Science Foundation (NSF) Directorate for Engineering (CFDA Program 47.041) awarded a $344,126 Project Grant to the University of Wisconsin System for a 3-year research project focused on developing new mathematical methods and computational tools to enable the use of complex data formats, such as visual and thermal images, for advanced model predictive control (MPC) systems. The key objectives are to: Integrate concepts from control theory, topology, machine learning, and Bayesian...
The Regents of the University of Michigan received a $480,000 project grant from the National Science Foundation under the Engineering federal grant program (CFDA 47.041) to develop foundational advances in robust reinforcement learning solutions and safe, constrained reinforcement learning methods with provable guarantees. The research will focus on advancing algorithmic solutions for reinforcement learning-based control in cyber-physical systems, using smart traffic signal control systems as...
This Project Grant award from the National Science Foundation (NSF) under the Computer and Information Science and Engineering (CFDA 47.070) program supports research on algorithms for optimization problems with uncertain or incomplete data inputs. The $484,600 grant awarded to the Regents of the University of Michigan will fund the design and analysis of parallelizable algorithms for stochastic optimization, as well as algorithms for stochastic optimization with unknown probability...
This Project Grant award, provided by the National Science Foundation (NSF) under the Engineering program (CFDA 47.041), will fund research to develop scalable and reliable coordination capabilities for embodied intelligent networks - collections of distributed autonomous agents that can sense, reason, communicate, and act. The $519,577 award to the Regents of the University of Michigan, with a performance period from Sep 1, 2024 to Aug 31, 2029, aims to address challenges in achieving...
This $315,000 federal Project Grant award from the National Science Foundation's Engineering program (CFDA 47.041) supports the development of new methods to robustly analyze and control large-scale networked systems with uncertain and variable delays. Specifically, the project will combine integral quadratic constraint and partial integral equation frameworks to enable accurate modeling and control of nonlinear systems with known and uncertain delay components. This work has direct applications...