MechE Inaugural Lecture: Towards learning for safety and coordination in uncertain dynamical systems
Face masks are recommended for in-person attendance in MED 0 1418.
Abstract: 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.
Materials science continues to rely on empirical knowledge for the design of advanced engineering alloys. At the macro-scale, the lack of rigorous phenomenology limits our ability to predict material properties. At the atomistic scale, mechanisms of kinetic and mechanical phenomena such as nucleation and deformation elude us. In this talk I will highlight recent advances in computational materials science that enable the rigorous and rational design of high-performance engineering alloys across length scales. Materials used in automotive, electrochemical and high-temperature applications will be used to illustrate the crucial insights that can be gained from first-principles models. The talk will conclude with a discussion of the challenges that must be overcome to accurately predict the properties of complex materials.
Bio: Anirudh received a B.Tech. in Metallurgical and Materials Engineering from the Indian Institute of Technology, Madras, a M.S in Materials Science and Engineering from the University of Michigan, Ann Arbor and a Ph.D. in Materials from the University of California, Santa Barbara. He set up the laboratory of materials design and simulation (MADES) at EPFL in 2022. His research interests are in the computational design and discovery of advanced engineering materials.