Project Grant 2535096
- 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...
- The National Science Foundation Division of Information and Intelligent Systems awarded Trustees of Dartmouth College $405,886 on January 1, 2027, for collaborative research on scalable cross-layer co-design for stable mixed-precision acceleration under the Computer and Information Science and Engineering program (CFDA 47.070). The project, performed in Hanover, New Hampshire through December 31, 2029, develops a unified high-level synthesis framework that combines numerical precision analysis...
- The National Science Foundation Division of Electrical, Communications and Cyber Systems awarded Massachusetts Institute of Technology $659,678 on April 1, 2026, under the CAREER: Trustworthy Learning-Enabled Autonomy project grant (NSF Engineering, CFDA 47.041). The project develops mathematical theory and efficient algorithms that allow autonomous systems to learn from data while reliably respecting safety constraints. The work addresses vulnerabilities in learning-enabled vehicles, robots,...
- The National Science Foundation Division of Information and Intelligent Systems awarded $228,950 to Trustees of Dartmouth College on September 1, 2026, under the Computer and Information Science and Engineering program (CFDA 47.070) to support research on attentional control of credit assignment in learning through September 1, 2031. The project develops and tests computational models of how humans adaptively control credit assignment strategies during learning, with the central hypothesis...
- This collaborative research project, funded by the National Science Foundation's (NSF) Division of Computer and Network Systems under the Computer and Information Science and Engineering program (CFDA 47.070), will develop robust optimization-based control algorithms and real-time scheduling techniques for cyber-physical systems (CPS) operating under computational constraints. The $563,370 award, effective August 1, 2025 through July 31, 2028, focuses on co-designing control algorithms robust to...
- The National Science Foundation Division of Computer and Network Systems awarded Georgia TECH Research Corp $360,000 on August 15, 2026, under the Computer and Information Science and Engineering program (CFDA 47.070) to develop a control-theoretic framework for reliable and robust deep neural networks with application to space cyber-physical systems. The project formulates deep neural network training as a feedback control design problem, leveraging optimal and robust control theory...
- The National Science Foundation Division of Computer and Network Systems awarded The Leland Stanford Junior University $320,000 on August 15, 2026, under the Computer and Information Science and Engineering program (CFDA 47.070) to develop a control-theoretic framework for certifying deep neural networks deployed on spacecraft. The project addresses the challenge of certifying learning-enabled components operating continuously aboard spacecraft by formulating deep neural network training as a...
- The National Science Foundation Division of Computer and Network Systems awarded the University of Texas at Austin $500,000 on July 1, 2026, under the Computer and Information Science and Engineering program (CFDA 47.070) for research on cloud robotics systems that dynamically balance edge computation and cloud inference under network constraints. The project develops algorithmic foundations enabling robotic fleets to continuously learn and adapt while managing the inherent trade-off between...
- The National Science Foundation Division of Computing and Communication Foundations awarded Duke University $420,000 on October 1, 2026, under the Computer and Information Science and Engineering program (CFDA 47.070) to develop theoretical and algorithmic frameworks for off-dynamics reinforcement learning—methods that enable intelligent systems to learn in simulated or indirect environments and transfer that knowledge reliably to real-world deployment scenarios with different transition...
- The National Science Foundation Division of Computer and Network Systems awarded the University of New Mexico $320,000 on August 15, 2026, under the Computer and Information Science and Engineering program (CFDA 47.070) to develop a control-theoretic framework for reliable and robust deep neural networks with application to space cyber-physical systems. The project formulates deep neural network training as a distributional control problem, leveraging optimal and robust control theory techniques...
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 autonomous systems—including drones and aerial robots—to leverage prior experience and 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 MPC. Research outputs include open-source software, student training in robotics and machine learning, and community activities to broaden access to cyber-physical systems. Applications span emergency response, environmental monitoring, and advanced manufacturing. Performance location is Hanover, New Hampshire. The period of performance runs from August 1, 2026, through July 31, 2029.
Mod # | Description | ReasonForModification | Federal Obligation | Date |
|---|---|---|---|---|
| Not listed | $498.8k | 8/10/26 |