Project Grant W911NF2410155

This Project Grant award from the National Science Foundation's (NSF) Computer and Information Science and Engineering (CISE) program, CFDA 47.070, provides $530,000 to The Trustees of the Stevens Institute of Technology in Hoboken, NJ to develop a physical computing testbed for collaborative, problem-based learning of cyber-physical systems (CPS) concepts. The research aims to explore and characterize the key threshold concepts required for CPS mastery, integrating student-centered...

This $200,000 National Science Foundation Project Grant supports research at Michigan State University to develop data-driven modeling techniques for securing cyber-physical systems. Funded under the Computer and Information Science and Engineering program (CFDA 47.070), the two-year award will create automated strategies using reinforcement learning to characterize attacker intent. Researchers will integrate defense approaches combining game theory, reinforcement learning and Bayesian...

Arizona State University was awarded a three-year $500,000 project grant from the National Science Foundation to develop neuro-symbolic learning and control tools for cyber-physical systems. The grant is funded through the NSF's Computer and Information Science and Engineering program, which supports research and education in all areas of computing, communications, and information science and engineering. Under this award, Arizona State University researchers will integrate machine learning,...

Federal Project Grant Award Summary The National Science Foundation's Computer and Network Systems division (under the Computer and Information Science and Engineering program, CFDA 47.070) awarded $250,000 to the University of California, Irvine for a three-year project (June 1, 2025 – May 31, 2028) titled "CPS: SMALL: Advanced Hyperdimensional and Symbolic Knowledge Transfer for Cyber-Physical Systems." The project delivers a novel neuro-symbolic framework that integrates...

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...

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 $161,841 project grant from the National Science Foundation's Computer and Information Science and Engineering program will support Northeastern University in developing theory and algorithmic tools for data-driven modeling and preview-based control to improve cyber-physical system safety. Specifically, the university will create a modeling formalism called "preview automata" to design provably-safe controllers leveraging predictive information from sensors, maps, forecasts or...

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,"...

This EAGER (Early-Concept Grants for Exploratory Research) award from the National Science Foundation's (NSF) Engineering program (CFDA 47.041) will develop generative artificial intelligence (AI) methods to learn from computational physics simulations and mathematical equations. The $300,000 project aims to expand the capabilities of large language models, such as OpenAI's ChatGPT and Microsoft's Copilot, to go beyond text-based learning and make predictions on complex, coupled physics problems...

Wayne State University was awarded a $296,254 Project Grant from the National Science Foundation Division of Electrical, Communications and Cyber Systems Engineering program to conduct research on physics-model-based neural networks from June 15, 2023 to May 31, 2026. Under this award, the university will develop a hybrid self-correcting physics-enhanced deep neural network framework called HYPHY-DNN. HYPHY-DNN aims to provide the performance benefits of deep learning models while...