Project Grant 31310021M0044

This $400,000 Project Grant awarded by the National Science Foundation (NSF) Division of Electrical, Communications and Cyber Systems aims to revolutionize the design of learning-enabled, safety-critical systems, with a focus on power systems. The project, titled "COLLABORATIVE RESEARCH: SLES: SAFETY UNDER DISTRIBUTIONAL SHIFT IN LEARNING-ENABLED POWER SYSTEMS", will develop proactive, antifragile systems that can anticipate and adapt to changes, utilize multi-agent systems for...

This $500,000 project grant, awarded on January 1, 2024 by the National Science Foundation (NSF) under the Computer and Information Science and Engineering (CISE) program (CFDA 47.070), aims to address the urgent need for end-to-end safety in learning-enabled autonomous systems across various application scenarios, such as self-driving cars and urban air mobility. The project, titled "COLLABORATIVE RESEARCH: SLES: GUARANTEED TUBES FOR SAFE LEARNING ACROSS AUTONOMY ARCHITECTURES,"...

The National Science Foundation (NSF) has awarded a $266,589 Project Grant under the Computer and Information Science and Engineering (CISE) program to the University of California, Berkeley to develop safe learning-enabled systems that can navigate uncertain environments. The project aims to create a two-phase design process that combines an offline robust synthesis phase with an online safety monitoring and adaptation phase, enabling provable end-to-end safety guarantees for learning-enabled...

This Project Grant award from the National Science Foundation (NSF) Division of Electrical, Communications and Cyber Systems supports research to develop low-complexity, safe learning-enabled algorithms for partially observable nonlinear systems with uncertain dynamics. The $400,000 award to Michigan State University aims to accomplish two key objectives: 1) Propose direct data-driven learning approaches for backup safe control policies in partially observable nonlinear systems, and 2) Introduce...

CAREER: Safety-Centered Multi-Agent Reinforcement Learning Wake Forest University received $520,013 in Project Grant funding from the National Science Foundation's Division of Information and Intelligent Systems (CFDA 47.070) awarded on July 15, 2025, with a completion date of June 30, 2030. This CAREER award supports research and development of a comprehensive safety framework for multi-agent reinforcement learning (MARL) systems. The project delivers four key innovations: (1) learning...

This National Science Foundation (NSF) Division of Electrical, Communications and Cyber Systems award, CFDA 47.041 Engineering, will provide $193,000 from September 1, 2024 to August 31, 2027 to New York University (NYU) to develop new theories and methodologies for safe reinforcement learning in domains such as robotics, autonomous driving, and power systems. The key products and services to be delivered under this Project Grant include: 1) Formulating safety measures as general objectives...

This $400,000 Project Grant awarded by the National Science Foundation (NSF) Division of Electrical, Communications and Cyber Systems under CFDA No. 47.041 Engineering aims to revolutionize the design of learning-enabled, safety-critical systems with a focus on power systems. The project at the University of California, Berkeley will introduce the concept of "antifragility" to promote system enhancement through change and uncertainty, rather than perceiving them as detriments. Key...

This $270,913 federal Project Grant award, funded by the National Science Foundation (NSF) under the Computer and Information Science and Engineering (CISE) program, supports research to develop qualitative and quantitative methodologies for assessing the safety of learning-enabled autonomous systems. The project, led by the Augusta University Research Institute, Inc. (AURI), will target foundational challenges in capturing uncertainties from environments and providing timely, comprehensive, and...

This Project Grant award from the National Science Foundation (NSF) Computer and Information Science and Engineering (CISE) Federal Grant Program (CFDA 47.070) will support research to establish a framework for designing and implementing safe learning-enabled systems. The $399,965 award to Cornell University, with a period of performance from October 1, 2024 to September 30, 2027, aims to develop methods for ensuring the safety of learning-enabled systems, even in complex operating environments,...

This Project Grant award of $285,694, provided by the National Science Foundation (NSF) under the Integrative Activities program (CFDA 47.083), aims to develop a safe online hierarchical learning framework for Open Radio Access Network (O-RAN) mobile networks. The key goals are to enable online resource allocation at near-real-time RICs using safe deep reinforcement learning, and online service orchestration at non-real-time RICs through robust Bayesian learning. Additionally, the project will...