Project Grant 2207547
- This Project Grant from the National Science Foundation's Computer and Information Science and Engineering program provides $600,000 to the University of Michigan to develop a new analytical method for analyzing large-scale stochastic systems from July 2022 to June 2025. The method aims to obtain non-asymptotic performance bounds for stochastic networks and systems using Lyapunov drift analysis combined with ideas from Stein's method, dimensionality reduction, and reproducing kernel Hilbert...
- 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 National Science Foundation Project Grant award of $231,879 provides funding from July 15, 2022 through June 30, 2025 to support research into the dynamics of stochastic networks. Under the Mathematical and Physical Sciences program (CFDA 47.049), the University of California, San Diego will analyze and develop methods for approximating, controlling, and interpreting complex stochastic network models. Key areas of focus include justifying and analyzing approximations like fluid and...
- 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 $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 $280,000 Project Grant award from the National Science Foundation's Engineering program (CFDA 47.041) will support the development of new theory and methods to address network-level uncertainty in the control of large-scale networked systems. The research will integrate random graph theory, particularly graphon theory, with structural system theory to model and understand uncertainty in the communication topology of multi-agent control systems. The key goals are to (1) formulate new...
- 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 (NSF) Engineering program (CFDA 47.041) provides $480,225.00 to the University of Illinois to support foundational research on nonlinear filtering and reinforcement learning (RL) algorithms. The goal is to develop new methods for analyzing the stability of nonlinear filters and apply these techniques to create a new class of efficient RL algorithms. The research plan has two main parts: 1) Developing stochastic stability theory...
- The University of Illinois was awarded a $500,000 Project Grant from the National Science Foundation Division of Information and Intelligent Systems to support research activities related to reinforcement learning in non-stationary environments. Specifically, the grant will fund the development of techniques for safe reinforcement learning with fast adaptation and disturbance prediction capabilities. The work advances the National Science Foundation's Computer and Information Science and...
- The University of Chicago received a $250,000 Project Grant award from the National Science Foundation Division of Computer and Network Systems. The grant is part of the NSF's Computer and Information Science and Engineering program (CFDA 47.070), which supports investigator-initiated research and education in computing, communications, and information science engineering. Under this award, the University will conduct research modeling modern network traffic from October 2021 through September...
This three-year project grant from the National Science Foundation's Computer and Information Science and Engineering program (CFDA 47.070) provides $600,000 to the University of Illinois to develop a new analytical method for analyzing large-scale stochastic systems. The method aims to obtain non-asymptotic performance bounds for stochastic networks and systems through a synthesis of tools from probability, machine learning, and stochastic networks. It will apply Lyapunov drift analysis combined with ideas from Stein's method, dimensionality reduction, and reproducing kernel Hilbert spaces. The university will apply this new method to robust and low-latency computing networks supporting complex machine learning workloads, as well as deep reinforcement learning algorithms for neural temporal-difference learning and actor-critic methods. The award was effective July 1, 2022 and is scheduled to conclude by June 30, 2025.
Mod # | Description | ReasonForModification | Federal Obligation | Date |
|---|---|---|---|---|
| Not listed | $300.0k | 6/15/22 |