Project Grant 2533775
- 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 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...
- The National Science Foundation awarded Princeton University a $411,476 project grant under the Computer and Information Science and Engineering program (CFDA 47.070) to develop the theoretical foundations of trust-centered resilience for distributed coordination and optimization of multi-agent systems in the presence of adversaries. Specifically, the funding will support research from June 2022 to May 2026 to establish analytical frameworks and efficient exploitation of stochastic information...
- This $1,722,089 Project Grant awarded by the National Science Foundation's (NSF) Computer and Information Science and Engineering (CFDA 47.070) program aims to develop rigorous, scientific mechanisms to enable resilience in large-scale cyber-physical systems (CPS) against various perturbations. The award to Purdue University, effective June 15, 2024 through May 31, 2029, will focus on modeling and understanding the impacts of unintended errors, security attacks, unexpected interactions, and...
- 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 $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...
- This Project Grant award from the National Science Foundation (NSF) Computer and Information Science and Engineering (CISE) Federal Grant Program (CFDA 47.070) is for a project titled "Understanding Trust Transfer and Dynamics Across Automation Levels - Automated Vehicles (AVs)." The objective is to develop a framework for understanding how trust propagates across different levels of vehicle automation, focusing on ensuring trust is appropriately calibrated to support safe and...
- The National Science Foundation (NSF) has awarded a $850,000 Project Grant under its Computer and Information Science and Engineering (CFDA 47.070) program to the University of California, Los Angeles (UCLA) to develop an efficient human-in-the-loop learning framework for human-centric cyber-physical systems (CPS). The 3-year project aims to create a novel approach to integrate human oversight and intervention into the training of CPS agents, such as assistive driving and exoskeleton systems, to...
- This $493,514 Project Grant award from the National Science Foundation (NSF) under the Computer and Information Science and Engineering (CFDA 47.070) program supports research to develop rigorous, scientific mechanisms that enable resilience in large-scale cyber-physical systems (CPS) against various types of perturbations. The project, titled "COLLABORATIVE RESEARCH: CPS: FRONTIER: CHORUS: RESILIENT DISTRIBUTED CPS THROUGH RATIONAL AND DYNAMIC DECISION-MAKING AMONG MULTIPLE...
- This $536,733 Project Grant award from the National Science Foundation's Computer and Information Science and Engineering (CISE) program (CFDA 47.070) supports research at Michigan State University to ensure privacy and truthfulness in self-interested multi-agent cyber-physical systems (CPS). The project aims to develop novel frameworks and technical approaches that simultaneously address privacy preservation and truthful behavior in distributed optimization and Nash equilibrium seeking...
This federal Project Grant award of $599,922 from the National Science Foundation's Engineering (CFDA 47.041) program supports research to develop a novel, scalable, and trust-aware bilevel optimization framework tailored for multi-user cyber-physical systems (CPS) operating under uncertainty and ambiguous user trust. The research aims to advance the design of smarter, safer, and more adaptive CPS by explicitly embedding human trust and behavioral uncertainty into optimization models. The project will explore three key directions: (1) modeling multi-user decisions dynamically with trust update, (2) finding optimal and risk-averse policies for bilevel programs under various uncertainties, and (3) analyzing the resilience of CPS. The outcomes have the potential to transform sectors where human-machine interaction is central, such as transportation, robotics, and disaster response. The award, granted to the Regents of the University of Michigan, will also support interdisciplinary student training, the creation of interactive educational games, and the development of new graduate-level courses on CPS optimization and computation.
Mod # | Description | ReasonForModification | Federal Obligation | Date |
|---|---|---|---|---|
| Not listed | $599.9k | 7/31/25 |