Maryam Kamgarpour

Biography

short: 
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. She is the recipient of NASA High Potential Individual Award, NASA Excellence in Publication Award, and the European Union (ERC) Starting Grant.

long: 
Maryam Kamgarpour is a professor in the School of Engineering of École Polytechnique Fédérale de Lausanne. Prior to EPFL, she served as a faculty at the University of British Columbia and at ETH Zürich. She 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 stochastic control and multiagent learning and control. Her theoretical research is motivated by fascinating control problems arising in intelligent transportation systems, robotics, and power grid systems.

She was awarded the European Union Starting Grant (2016-2021) to advance her research on control and game theory for integrating renewable energy into the power system. Her work on distributed control received the IEEE Transactions on Control of Network Systems Outstanding Paper Award (2022). She collaborated with NASA on safe and fuel-efficient aircraft trajectory design, and for this work received the NASA High Potential Individual Award (2010).

She is passionate about understanding and addressing fundamental problems in control, and mentoring students to work with her on these problems. Her publications have contributed to theory of hybrid systems (reachability, safety and optimal control), distributed control (the role of information structure), game theory (learning equilibria under bandit feedback), mechanism design (coalition-proofness, price of anarchy), safe zeroth-order learning, inverse reinforcement learning and multiagent reinforcement learning.