Project Grant 2440920

Award Date 6/1/25
Completion Date 5/31/30
Dollars Obligated $233K
Federal Grant Program
47.070
Assistance Type
Project Grant
Place of Performance
Gainesville, FL 32611, USA
Similar Awards
The University of Florida (UF) Division of Sponsored Research was awarded a $229,988 Project Grant on June 15, 2024 from the National Science Foundation's (NSF) Computer and Information Science and Engineering (CISE) program (CFDA 47.070). The grant supports a research project titled "COLLABORATIVE RESEARCH: CPS: SMALL: NEURO-SYMBOLIC BRIDGE: FROM PERCEPTION TO ESTIMATION & CONTROL". The project aims to develop methods for aligning the uncertainty in neural network-based perception...
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 Project Grant award of $213,679 was provided by the National Science Foundation (NSF) under the Computer and Information Science and Engineering (CFDA 47.070) program. The funding supports research to develop a compositional framework and computational tools for designing and verifying the safety and robustness of cyber-physical systems (CPS) that utilize machine learning algorithms. The project aims to create specification formalisms, modeling techniques, and verification/synthesis methods...
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 $599,943 Project Grant awarded by the National Science Foundation's (NSF) Computer and Information Science and Engineering (CISE) program (CFDA 47.070) funds research to develop new techniques for ensuring reliable, real-time communication in critical cyber-physical systems like aircraft, automobiles, and industrial control networks. The key innovations involve creating scalable, adaptive, and resilient models, algorithms, and software designs to provide robust end-to-end quality-of-service...
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 $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, valued at $500,000 and provided by the National Science Foundation (NSF) under the Computer and Information Science and Engineering (CFDA 47.070) program, supports research by the Regents of the University of Michigan to develop new theories, algorithms, and tools for efficient and robust control of complex nonlinear cyber-physical systems. The key objectives are to enable verified, correct-by-construction control and monitoring of safety-critical systems such as...
The National Science Foundation (NSF) awarded a 5-year, $214,287 Project Grant under the Computer and Information Science and Engineering (CFDA 47.070) program to The Research Foundation For The State University Of New York (RF SUNY), doing business as Stony Brook University. The goal of this research is to advance the foundations of formal methods in order to make formal verification of AI-based cyber-physical systems (CPS) practical. The project investigates approximation approaches where an...
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 $232,603 Project Grant was awarded by the National Science Foundation (NSF) under the Computer and Information Science and Engineering (CFDA 47.070) program. The grant will fund a research project at the University of Florida aimed at transforming the management of assumptions in learning-enabled cyber-physical systems (CPS). The project will develop techniques, tools, and a catalog of typical assumptions to improve the performance and safety of CPS in unforeseen situations. Key goals include: (1) discovering and representing relevant assumptions of learning-enabled CPS, (2) validating these assumptions across offline and online settings, and (3) enhancing decision-making and control to recover from online violations of assumptions. The research will be evaluated on small-scale autonomous racing, underwater vehicles, and modeling autonomous street traffic. This project represents a major step towards building assumption-aware CPS that can behave with an understanding of their own assumptions and limitations.

Generated 4/29/25, 5:11 AM