Project Grant 2535097
- The National Science Foundation Division of Computer and Network Systems awarded Trustees of Dartmouth College $498,819 on August 1, 2026, under the Computer and Information Science and Engineering program (CFDA 47.070) to develop theoretically grounded, computationally efficient methods for safe multi-task learning and control on resource-constrained autonomous systems. The project integrates meta-learning, adaptive control, model predictive control (MPC), and embedded optimization to enable...
- This three-year, $240,000 Project Grant from the National Science Foundation's Division of Electrical, Communications and Cyber Systems, under the Engineering program (CFDA 47.041), will support collaborative research between Columbia University and the University of Pennsylvania on scalable and communication-efficient learning-based distributed control. The researchers will develop a foundational and integrated theory of distributed learning-enabled control and approximated distributed...
- The National Science Foundation Division of Computer and Network Systems awarded New York University $600,000 on March 1, 2026, under the Computer and Information Science and Engineering program (CFDA 47.070) for a U.S.–Ireland research and development partnership developing integrated modeling, analysis, and control frameworks to enhance resilience and safety in industrial control systems under cyber and physical threats. The project, performed in New York, New York, addresses how attacks...
- The National Science Foundation Division of Computer and Network Systems awarded The Trustees of Columbia University in the City of New York $410,000 on October 1, 2026, under the Computer and Information Science and Engineering program (CFDA 47.070) to develop methods and tools for verifying the trustworthiness of pre-trained AI models before their incorporation into scientific workflows and operational systems. The project addresses three major security vulnerabilities in the machine...
- The National Science Foundation awarded Columbia University a $500,000 Project Grant under the Engineering (47.041) federal grant program. The grant will fund research towards developing scale-invariant identification and synthesis algorithms for distributed control of networked systems using randomization techniques. Specifically, the university will conduct foundational research in three areas: 1) learning dynamical system models from partially observed data, 2) designing robust and optimal...
- 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 three-year $240,000 Project Grant from the National Science Foundation's Division of Electrical, Communications and Cyber Systems, under the Engineering program (CFDA 47.041), will support research to advance the practical application of distributed learning-enabled control systems. The University of Pennsylvania and Columbia University will collaborate on developing foundational theory and integrated approaches for scalable and communication-efficient distributed control. This includes...
- The National Science Foundation Division of Electrical, Communications and Cyber Systems awarded Rensselaer Polytechnic Institute $299,935 on July 1, 2026, under the Engineering program (CFDA 47.041) to develop decentralized multi-agent reinforcement learning algorithms that enable multiple autonomous decision-makers to learn safe and efficient operation in dynamic, competitive environments without relying on central coordination. The project addresses foundational challenges in safe learning...
- The National Science Foundation Division of Information and Intelligent Systems awarded a $545,980 Project Grant to Princeton University from August 1, 2021 through July 31, 2026 under the Computer and Information Science and Engineering program (CFDA 47.070). The grant funds research at Princeton University to develop generalization and safety guarantees for learning-based control of robots. The Computer and Information Science and Engineering program supports investigator-initiated research...
- The National Science Foundation Division of Computer and Network Systems awarded Vanderbilt University $151,766 on October 1, 2026, to host a multidisciplinary workshop developing a ten-year research roadmap for artificial intelligence-enabled cyber-physical systems and intelligent control. The workshop brings together approximately 50 leading researchers and innovators in cyber-physical systems, artificial intelligence, control theory, mechanics, and related fields to document advances and...
The National Science Foundation Division of Computer and Network Systems awarded Columbia University $515,235 on August 1, 2026, under the Computer and Information Science and Engineering program (CFDA 47.070) to develop theoretically grounded methods for safe multi-task learning and control on resource-constrained autonomous systems. The project develops a computationally efficient framework integrating meta-learning, adaptive control, model predictive control, and embedded optimization to enable autonomous systems—particularly small aerial robots—to rapidly adapt existing controllers to new tasks, environmental variations, or hardware degradation while guaranteeing safety, stability, and performance. The first research thrust establishes model-free meta-learning foundations for stochastic, partially observed control systems with formal convergence, stability, and sample-complexity guarantees, extending these methods to distributed multi-task learning while ensuring end-to-end robustness against control, planning, and perception noise. The second thrust enables safe, real-time personalization of controllers via constraint-aware optimization and hardware-software co-design, extracting system models to enable fast edge-based model predictive control. The research advances autonomous systems for emergency response, environmental monitoring, and advanced manufacturing by enabling safer, resource-efficient autonomy on edge devices that make real-time decisions without cloud access. The project will produce open-source software, train students across robotics and machine learning, and support community activities to broaden access to cyber-physical systems. Performance period runs from August 1, 2026, through July 31, 2029, with place of performance in New York, New York.
Mod # | Description | ReasonForModification | Federal Obligation | Date |
|---|---|---|---|---|
| Not listed | $515.2k | 8/10/26 |