Project Grant 2544396
- This National Science Foundation (NSF) CAREER Award under CFDA 47.041 Engineering program provides $517,612 to the University of Texas at Austin from March 1, 2025 to February 28, 2029. The project aims to develop new foundations of scalable and resilient distributed reinforcement learning for real-time autonomous cooperation in open multi-agent systems. The key goals are to design learning and control methods that enable agents to interact effectively in open systems, adapt to time-varying...
- This CAREER award from the National Science Foundation (NSF) Engineering program (CFDA 47.041) provides $687,382 in funding to the Massachusetts Institute of Technology (MIT) to support research and education focused on the foundations of the next generation of artificial intelligence (AI) for engineering design. The project aims to establish deep generative models (DGMs) that can effectively address challenges specific to engineering design at different scales, complexity, and disciplinarity....
- 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,"...
- Federal Grant Award Summary Carnegie Mellon University received a $500,000 Project Grant from the National Science Foundation (NSF) Division of Electrical, Communications and Cyber Systems under the Engineering program (CFDA 47.041), awarded July 1, 2025, with completion targeted for June 30, 2030. This CAREER award supports fundamental research on safety, human alignment, and interaction-awareness in autonomous control systems operating across multiple domains including transportation,...
- This federal Project Grant award of $800,000 from the National Science Foundation's (NSF) Computer and Information Science and Engineering (CISE) program (CFDA 47.070) is supporting research to develop methods for certifying the safety of autonomous systems that use deep learning-enabled perception, prediction, and control components. The key goals of the project are: (1) to develop techniques for learning safety certificates and control policies for these types of learning-enabled autonomous...
- CAREER: Safety-Centered Multi-Agent Reinforcement Learning Wake Forest University received $520,013 in Project Grant funding from the National Science Foundation's Division of Information and Intelligent Systems (CFDA 47.070) awarded on July 15, 2025, with a completion date of June 30, 2030. This CAREER award supports research and development of a comprehensive safety framework for multi-agent reinforcement learning (MARL) systems. The project delivers four key innovations: (1) learning...
- This National Science Foundation (NSF) Faculty Early Career Development (CAREER) Program award provides $581,320 in funding to the University of Vermont (UVM) over a 5-year period from June 1, 2024 to May 31, 2029. The research project, titled "A Universal Framework for Safety-Aware Data-Driven Control and Estimation", aims to develop a framework for the simultaneous design of control policies and safety measures for complex systems like robotics and power systems using data-driven...
- The Trustees of the University of Pennsylvania received a $500,000 Project Grant award from the National Science Foundation Division of Electrical, Communications and Cyber Systems on February 15, 2021 to support research titled "CAREER: TOWARDS A THEORY OF ROBUST LEARNING & CONTROL FOR SAFETY-CRITICAL AUTONOMOUS SYSTEMS" through January 31, 2026. This award will fund research under the NSF Engineering program (CFDA #47.041) to develop a theoretical framework for robust learning...
- This collaborative research project, funded by the National Science Foundation (NSF) Division of Information and Intelligent Systems under the Computer and Information Science and Engineering (CISE) program (CFDA 47.070), addresses critical safety and robustness challenges in autonomous multi-agent systems. Awarded to the University of California, Berkeley on August 1, 2025, with total funding of $200,000 and a completion date of July 31, 2028, the project develops a scalable framework for...
- This National Science Foundation (NSF) Division of Electrical, Communications and Cyber Systems award, CFDA 47.041 Engineering, will provide $193,000 from September 1, 2024 to August 31, 2027 to New York University (NYU) to develop new theories and methodologies for safe reinforcement learning in domains such as robotics, autonomous driving, and power systems. The key products and services to be delivered under this Project Grant include: 1) Formulating safety measures as general objectives...
This $659,678 NSF CAREER (Faculty Early Career Development) award, funded through the Engineering program (CFDA 47.041) and administered by the Division of Electrical, Communications and Cyber Systems, supports a five-year project (April 1, 2026 – March 31, 2031) at MIT to develop foundational technologies for trustworthy learning-enabled autonomous systems. The primary deliverables include new mathematical theory and efficient algorithms for constraint-satisfying learning, uncertainty-aware decision-making frameworks, and safe coordination tools for multi-agent systems. The project will produce hard-constrained neural networks (HARDNET) with differentiable projection layers that guarantee constraint satisfaction during training and deployment, as well as run-time uncertainty monitoring capabilities and scalable decentralized decision-making tools applicable across individual neural networks, learning-based controllers, and large multi-agent coordination systems. Beyond core research outputs, the project will deliver open-source tools and datasets to advance the field, along with integrated education and outreach initiatives spanning K-12 through graduate-level training and public engagement through interactive demonstrations. The research aims to enhance safety and reliability in autonomous transportation, robotics, and engineered infrastructure systems while addressing critical national priorities in AI-enabled autonomy for both civilian and security applications.
Mod # | Description | ReasonForModification | Federal Obligation | Date |
|---|---|---|---|---|
| Not listed | $659.7k | 3/19/26 |