Brian Williams
Professor, Department of Aeronautics and Astronautics
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Brian C. Williams is a professor in MIT’s Department of Aeronautics and Astronautics and a member of the Computer Science and Artificial Intelligence Laboratory (CSAIL), where he leads the Model-based Embedded and Robotic Systems group. He is a pioneer in the fields of risk-aware autonomous systems, qualitative reasoning, and model-based diagnosis, and co-led the development of the NASA Remote Agent system, demonstrated on Deep Space One. His research focuses on creating long-lived systems that seek information autonomously and act safely, while commanding, diagnosing and repairing themselves using fast, commonsense reasoning and machine learning. His interests include neural symbolic systems, active learning in risky environments, reasoning at reactive time scales, cooperative and space robotics, intelligent embedded systems, model-based reactive planning, execution and diagnosis, data-driven exploratory modeling, semantic interpretation and hybrid system control. Before coming to MIT, he worked at the Xerox Palo Alto Research Center and NASA Ames Research Center. He earned an SB and a PhD in electrical engineering and computer science from MIT.
Selected Publications
- Chen, J., Williams, B. C., Fan, C. (2021). Optimal mixed discrete-continuous planning for linear hybrid systems. International Conference on Hybrid Systems: Computation and Control.
- Huang, X., McGill, S. G., DeCastro, J. A., Fletcher, L., Leonard, J. J., Williams, B. C., Rosman, G. (2020). DiversityGAN: Diversity-Aware Vehicle Motion Prediction via Latent Semantic Sampling. IEEE Robotics and Automation Letters.
- Timmons, E. and Williams, B. C. (2020). Best-First Enumeration Based on Bounding Conflicts, and its Application to Large-scale Hybrid Estimation. Journal of Artificial Intelligence Research 67, 1-34.
Media
- April 23, 2020: MIT News, Reporting tool aims to balance hospitals’ Covid-19 load.
- January 30, 2019: MIT News, Engineers program marine robots to take calculated risks.
- February 15, 2016: MIT News, Automatic contingency planning.