Maryam Kamgarpour

EPFL STI IGM SYCAMORE
ME C1 400 (Bâtiment ME)
Station 9
1015 Lausanne

Maryam Kamgarpour is an associate professor in the School of Engineering of École Polytechnique Fédérale de Lausanne. Prior to joining 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 the University of Waterloo, Canada. Her research focuses on developing theory and algorithms for control and learning in stochastic and multi-agent systems, as well as inverse control and learning, and mechanism design. These theoretical directions are motivated by control challenges in intelligent transportation systems, robotics, and power grid applications.

She has been awarded the European Union Consolidator Grant (2026-2031), the 2024 European Control Award, the European Union Starting Grant (2016-2021), an IEEE Transactions on Control of Network Systems Outstanding Paper  (2022), NASA High Potential Individual Award (2010) and NASA Excellence in Publication Award (2010). She is an ELLIS Fellow and an associate editor for IEEE Transactions on Automatic Control.

Awards

European Control Award

European Control Association

2024

Infoscience

Teaching & PhD

PhD Students

Giulio Salizzoni, Tingting Ni, Saurabh Dilip Vaishampayan, Andreas Schlaginhaufen, Kai Ren, Anna Maria Maddux, Gabriel Vallat, Philip Jordan, Yasaman Zolfimoselo, Daniele De Cecco, Gregorio Valenti

Past EPFL PhD Students as codirector

Baiwei Guo

Courses

Foundations of artificial intelligence

ME-390

This course provides the students with 1) a set of theoretical concepts to understand the machine learning approach; and 2) a subset of the tools to use this approach for problems arising in mechanical engineering applications.

Multiagent decision-making and control

ME-429

Students will be able to formulate a multi-agent decision-making problem in static and dynamic environments as a game and apply relevant mathematical theories and algorithms to analyze the interaction of the agents and predict the outcome of the decision-making problems.