MechE Inaugural Lecture: Towards learning for safety and coordination in uncertain dynamical systems


Event details

Date 20.09.2022 12:0013:00  
Speaker Prof. Maryam Kamgarpour, Systems Control And Multiagent Optimization Research Laboratory (SYCAMORE), Institute of Mechanical Engineering (IGM), School of Engineering (STI), EPFL
Location Online
Category Inaugural lectures - Honorary Lecture
Event Language English
Face masks are recommended for in-person attendance in MED 0 1418.

We began using automation to help us with simple and repetitive tasks. Now, we are introducing automation in increasingly complex and safety-critical tasks: intelligent transportation systems, smart power grid, personalized medicine, healthcare robotics, architecture.... Motivated by the needs of the autonomy in these domains, my research is on design of algorithms with guaranteed performance. In particular, I will discuss my group’s work on two challenges: 1) guaranteeing safety of the data-driven optimization and control algorithms; 2) coordinating interactions in a multi-agent decision-making scenario. I will illustrate our theory and algorithms with applications arising in robotics, autonomous driving and power systems.

Bio: Maryam Kamgarpour holds a Doctor of Philosophy in Engineering from the University of California, Berkeley and a Bachelor of Applied Science from University of Waterloo, Canada. Her research is on safe decision-making and control under uncertainty, game theory and mechanism design, mixed integer and stochastic optimization and control. Her theoretical research is motivated by control challenges arising in intelligent transportation networks, robotics, power grid systems and healthcare. Prior to joining EPFL, she was a non-tenure track assistant professor at ETH Zurich and a tenure-track assistant professor at UBC, Canada. She is the recipient of NASA High Potential Individual Award, NASA Excellence in Publication Award, and the European Union (ERC) Starting Grant.

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Practical information

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MechE Inaugural Lecture: Towards learning for safety and coordination in uncertain dynamical systems