This Project Grant award of $300,000 from the National Science Foundation's (NSF) Computer and Information Science and Engineering (CISE) Federal Grant Program supports research on developing machine learning models for structural decision-making. The goal is to create models that can capture an agent's preferences and understanding of environmental dynamics, enabling better prediction and adaptation of decision-making in complex real-world scenarios. The research will explore methods for...
The National Science Foundation Division of Electrical, Communications and Cyber Systems awarded a $500,000 Project Grant to Texas A&M Engineering Experiment Station, doing business as Tees, to support research towards a principled framework for resilient, data efficient and scalable reinforcement learning for control. The award period is from February 1, 2021 through January 31, 2026. The research is funded under the NSF Directorate for Engineering's Engineering program (CFDA 47.041), which...
This Project Grant award from the National Science Foundation (NSF) Computer and Information Science and Engineering (CISE) program (CFDA 47.070) will develop a cross-level methodology to enable state-of-the-art reinforcement learning techniques to run on resource-constrained edge devices. The $204,205 award to the Texas A&M Engineering Experiment Station (Tees) will integrate novel approximate computing circuit techniques and flash device-based computing to significantly improve computing...
This National Science Foundation (NSF) Computer and Information Science and Engineering (CFDA 47.070) Project Grant award of $600,000 to the Massachusetts Institute of Technology (MIT) supports research into developing better algorithms for machine learning problems that involve sequential data with rich dependency structures. The project will explore learning methods for linear dynamical systems, graphical models, and hidden Markov models, with the goal of proving rigorous theoretical...
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 $101,476, supports research to develop reliable decision-making algorithms for machine learning applications in complex systems. The 5-year project, which commenced on April 1, 2025, aims to address the challenge of ensuring safety and performance when deploying machine learning predictions in feedback loops, such as in weather...
This Project Grant award of $160,673 from the National Science Foundation's (NSF) Computer and Information Science and Engineering (CISE) program supports research to combine algorithms and machine learning to improve decision-making under uncertainty. The project, led by New York University (NYU), will explore incorporating machine-learned predictions into algorithm design as well as developing learning models optimized for specific algorithmic objectives. This work aims to create a...
This Project Grant award from the National Science Foundation (NSF) Computer and Information Science and Engineering (CISE) Program (CFDA 47.070) provides $300,000 to the Regents of the University of California at Riverside to conduct research on adapting foundation models for multimodal sequential decision-making. The project aims to develop novel techniques and methods to leverage foundation models, which are complex neural networks trained on large datasets, to improve the performance of...
This Project Grant award of $476,440 from the National Science Foundation's (NSF) Computer and Information Science and Engineering (CISE) Federal Grant Program supports research into developing new algorithms and approaches for unsupervised and autonomous reinforcement learning of skills by artificial intelligence (AI) models. The key objectives of the research are to: 1) create new algorithms to discover small, reusable skills that can be rapidly combined to solve complex tasks; 2) develop...
This $128,238 federal Project Grant award from the National Science Foundation's (NSF) Computer and Information Science and Engineering (CISE) program supports research at the University of Texas at Austin to advance reinforcement learning (RL) through the development of a unified, principled off-policy algorithm framework. The research aims to enable more efficient, large-scale real-world sequential decision-making applications such as self-driving cars, industry automation, and natural...
This Project Grant award from the National Science Foundation's Engineering program (CFDA 47.041) provides $356,488 to the Texas A&M Engineering Experiment Station (Tees) to develop a resilient reinforcement learning (RL) framework for managing heterogeneous multi-agent systems in complex and structured environments. The research aims to produce scalable and computationally-efficient RL algorithms with rigorous convergence and complexity analysis for applications like interference management...