Project Grant 2452829
- Rice University received a $214,533 Project Grant award from the National Science Foundation Division of Mathematical Sciences under the Mathematical and Physical Sciences federal grant program (CFDA 47.049) to support collaborative research on risk-sensitive control of stochastic networks. The two-year award beginning January 1, 2022 will advance mathematical analysis in stochastic control and develop advanced methods and algorithms to provide risk-mitigating solutions for operational...
- This Project Grant award from the National Science Foundation's (NSF) Engineering program (CFDA 47.041) provides $500,000 to Carnegie Mellon University to develop techniques for mitigating various risks in the control of autonomous systems operating in uncertain and interactive environments. The five-year project, awarded on July 1, 2025, aims to: 1) quantify long-term risks despite latent variables and limited data, and 2) develop efficient control techniques that provide long-term assurance...
- The National Science Foundation Division of Electrical, Communications and Cyber Systems awarded a $500,000 Project Grant to Texas A&M Engineering Experiment Station, doing business as Tees, to support research towards a principled framework for resilient, data efficient and scalable reinforcement learning for control. The award period is from February 1, 2021 through January 31, 2026. The research is funded under the NSF Directorate for Engineering's Engineering program (CFDA 47.041), which...
- This federal Project Grant award of $360,000.00 from the National Science Foundation (NSF) Engineering program (CFDA 47.041) supports the development of a unified framework for designing efficient algorithms for risk-sensitive reinforcement learning (RL) using coherent risk measures. The research project, conducted by the University of California, Davis, aims to systematically employ coherent risk measures to develop RL algorithms suitable for safety-critical applications where rare but...
- This federal Project Grant award from the National Science Foundation's Engineering program (CFDA 47.041) provides $193,000 to the Trustees of Princeton University over a 3-year period starting September 1, 2024. The funding supports collaborative research on developing safe reinforcement learning techniques that can be applied in domains like robotics, autonomous driving, and power systems. The key research thrusts include: 1) training robust policies using distributionally robust approaches;...
- This National Science Foundation (NSF) Engineering (CFDA 47.041) grant award, titled "CAREER: DATA-ENABLED NEURAL MULTI-STEP PREDICTIVE CONTROL (DEMUSPC): A LEARNING-BASED PREDICTIVE AND ADAPTIVE CONTROL APPROACH FOR COMPLEX NONLINEAR SYSTEMS", provides $655,248 in funding to the University of Houston System from September 2024 through August 2029. The project aims to conduct fundamental research to develop data-driven and learning-based predictive and adaptive control approaches, with...
- 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 Project Grant award from the National Science Foundation's Engineering program (CFDA 47.041) provides $356,488 to the Texas A&M Engineering Experiment Station (Tees) to develop a resilient reinforcement learning (RL) framework for managing heterogeneous multi-agent systems in complex and structured environments. The research aims to produce scalable and computationally-efficient RL algorithms with rigorous convergence and complexity analysis for applications like interference management...
- This $250,000 Project Grant award from the National Science Foundation (NSF) Engineering program (CFDA 47.041) aims to address challenges in stochastic nonlinear control and learning for dynamical systems through a novel "Spectral Dynamic Embedding" approach. Led by the Georgia Tech Research Corporation, the research intends to develop computationally efficient control algorithms suitable for applications in robotics, aerospace, manufacturing, and beyond. The key innovations involve...
- This Project Grant award from the National Science Foundation (NSF) Division of Electrical, Communications and Cyber Systems supports research to develop low-complexity, safe learning-enabled algorithms for partially observable nonlinear systems with uncertain dynamics. The $400,000 award to Michigan State University aims to accomplish two key objectives: 1) Propose direct data-driven learning approaches for backup safe control policies in partially observable nonlinear systems, and 2) Introduce...
This Project Grant award from the National Science Foundation's (NSF) Engineering program (CFDA 47.041) provides $140,000 in funding to William Marsh Rice University to develop adaptive control and learning algorithms for risk-sensitive control of complex, networked systems. The research aims to create algorithms that can learn from data and adapt in real-time, while accounting for uncertainties and risks in performance metrics. The project will advance computational and learning methods for risk-sensitive control of Markov chains and diffusions, with applications in areas such as service operations, manufacturing, healthcare, telecommunications, and cloud computing. Additionally, the project will enhance STEM education by integrating the cutting-edge research into undergraduate and graduate curricula, preparing students with the advanced skills needed to lead in fields like AI, operations research, and industrial engineering. The award period runs from September 1, 2025 to August 31, 2028.
Mod # | Description | ReasonForModification | Federal Obligation | Date |
|---|---|---|---|---|
| Not listed | $140.0k | 9/2/25 |