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Decision making for safety critical systems

Mykel Kochenderfer ( Stanford University )
  • 11Jun
    16:00 11th June 2026 ( Trinity Term 2026 )
    Seminar Room 051

Building robust decision making systems for safety critical systems is challenging because decisions must be made based on imperfect information about the environment and with uncertainty about how the environment will evolve. In addition, these systems must carefully balance safety with other considerations, such as operational efficiency. Typically, the space of edge cases is vast, placing a large burden on human designers to anticipate problem scenarios and develop ways to resolve them. This talk discusses major challenges associated with ensuring computational tractability and establishing trust that our systems will behave correctly when deployed in the real world. We will outline some methodologies, including some involving large language models, for addressing these challenges and point to some research applications that can serve as inspiration for building safer systems.

Speaker bio

Mykel Kochenderfer is an Associate Professor of Aeronautics and Astronautics at Stanford University. He is the director of the Stanford Intelligent Systems Laboratory (SISL), conducting research on advanced algorithms and analytical methods for the design of robust decision making systems. His research contributed to what became the international standard for aircraft collision avoidance. Prof. Kochenderfer is a co-director of the Center for AI Safety and senior fellow of the Stanford Institute for Human-Centered Artificial Intelligence (HAI). He is editor-in-chief of the Journal of Artificial Intelligence Research. He is an author of the textbooks Decision Making under Uncertainty: Theory and Application (MIT Press, 2015), Algorithms for Optimization (MIT Press, 2019), Algorithms for Decision Making (MIT Press, 2022), and Algorithms for Validation (MIT Press, 2026).

 

 

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