This Project Grant award of $226,180, provided by the National Science Foundation (NSF) Computer and Information Science and Engineering (CISE) Federal Grant Program (CFDA 47.070), supports research to enhance the security of cyber-physical systems (CPS) that utilize reinforcement learning, such as autonomous vehicles. The project will develop innovations to achieve safe and secure reinforcement learning, protecting users from system vulnerabilities and systems from attacks. Key focus areas...
This $316,963 Project Grant award from the National Science Foundation's (NSF) Computer and Information Science and Engineering (CISE) program will support research to enhance the safety and reliability of autonomous vehicles. The project aims to thoroughly examine and improve the controller and machine learning components of autonomous driving systems through a combination of model-based and data-driven approaches. The research will focus on identifying spatial and temporal vulnerabilities that...
This $300,027 Project Grant awarded by the National Science Foundation's (NSF) Computer and Information Science and Engineering (CISE) program supports research to develop secure and trustworthy learning-based control systems for cyber-physical systems (CPS). The project aims to: a) Develop a real-time reward manipulation scheme for learning-based controllers b) Design multi-level attack schemes on reward signals in a distributed CPS control architecture c) Develop data-enabled strategies for...
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 National Science Foundation project grant of $597,585 will fund research into co-designed control and scheduling adaptation for cyber-physical system safety and performance from April 2023 through March 2026. The award is provided through the Computer and Information Science and Engineering program (CFDA 47.070), which supports investigator-initiated research and education in all areas of computing, communications, and information science and engineering. Specifically, a team led by...
The National Science Foundation (NSF) awarded a $188,410 Project Grant under the Computer and Information Science and Engineering (CISE) program to the University of California, Berkeley. This 18-month grant, which began on January 1, 2024, supports the development of risk-aware interactive control and planning algorithms to achieve safe cyber-physical-human (CPS-H) systems. The key objectives of this research project are: (i) creating computationally-tractable risk-aware trajectory planning...
This Project Grant from the National Science Foundation's Computer and Information Science and Engineering program (CFDA 47.070) provides $592,000 to Purdue University to support collaborative research on co-designed control and scheduling adaptation for cyber-physical system safety and performance from April 2023 through March 2026. The award aims to develop new models, analyses, infrastructure and metrics to represent and account for interdependencies between control and scheduling in...
This Project Grant award, provided by the National Science Foundation's (NSF) Computer and Information Science and Engineering (CISE) Federal Grant Program (CFDA 47.070), supports research to characterize the vulnerabilities of learning-based controllers in networked cyber-physical systems (CPS). The $149,985 award will enable the University of Alabama in Huntsville (UAH) to develop real-time reward manipulation schemes, multi-level attack strategies, and data-enabled detection methods to...
This $149,343 Project Grant awarded by the National Science Foundation's (NSF) Computer and Information Science and Engineering (CFDA 47.070) Federal Grant Program supports the development of a new quantitative verification approach for temporal properties of learning-enabled cyber-physical systems (LE-CPS). The key objectives are to: Develop a qualitative and quantitative verification approach for LE-CPS at the system level based on Probstar reachability, providing the precise probability of...
This three-year Project Grant from the National Science Foundation's Division of Computer and Network Systems and Computer and Information Science and Engineering program (CFDA 47.070) provides $500,000 to Stanford University to develop risk-aware planning and control strategies for safe human-cyber physical system interactions. The award will advance the state-of-the-art in cyber-physical-human system planning and control through computationally tractable risk-aware trajectory planning...