Alexandre Bayen is the Liao-Cho Professor of Engineering at UC Berkeley. He is a Professor of Electrical Engineering and Computer Science(link is external), and Civil and Environmental Engineering(link is external). He is currently the Director of the Institute of Transportation Studies(link is external) (ITS). He is also a Faculty Scientist in Mechanical Engineering, at the Lawrence Berkeley National Laboratory(link is external) (LBNL). He received the Engineering Degree in applied mathematics from the Ecole Polytechnique, France, in 1998, the M.S. and Ph.D. in aeronautics and astronautics from Stanford University in 1999 and 2004, respectively. He was a Visiting Researcher at NASA Ames Research Center from 2000 to 2003. Between January 2004 and December 2004, he worked as the Research Director of the Autonomous Navigation Laboratory at the Laboratoire de Recherches Balistiques et Aerodynamiques, (Ministere de la Defense, Vernon, France), where he holds the rank of Major. He has been on the faculty at UC Berkeley since 2005. Bayen has authored two books and over 200 articles in peer reviewed journals and conferences. He is the recipient of the Ballhaus Award from Stanford University, 2004, of the CAREER award from the National Science Foundation, 2009 and he is a NASA Top 10 Innovators on Water Sustainability, 2010. His projects Mobile Century and Mobile Millennium received the 2008 Best of ITS Award for ‘Best Innovative Practice’, at the ITS World Congress and a TRANNY Award from the California Transportation Foundation, 2009. Mobile Millennium has been featured more than 200 times in the media, including TV channels and radio stations (CBS, NBC, ABC, CNET, NPR, KGO, the BBC), and in the popular press (Wall Street Journal, Washington Post, LA Times). Bayen is the recipient of the Presidential Early Career Award for Scientists and Engineers (PECASE) award from the White House, 2010. He is also the recipient of the Okawa Research Grant Award, the Ruberti Prize from the IEEE, and the Huber Prize from the ASCE.
- Stanford University, Stanford, California, Ph.D. in Aeronautics and Astronautics, Jan. 2004
- Stanford University, Stanford, California, M.S. in Aeronautics and Astronautics, June 1999
- Ecole Polytechnique, France, Eng. Deg. in Applied Mathematics, July 1998
My general area of research lies at the intersection of control, optimization, and machine learning. My current applications include mobile robotics, transportation, and engineering. My past applications include connected health and sensing of water systems. The problems I am generally interested in focus on the integration of novel data sources into mathematical learning models. They also involve the application of machine learning algorithms to large scale mobility problems. The techniques I use include game theory, convex optimization, network optimization, deep reinforcement learning, partial differential equations, and numerical analysis.
The research lab currently has two major focuses:
FLOW is a deep reinforcement learning framework implemented on AWS EC2 and used for learning and optimization over microsimulation tools for traffic flow. Its main application includes mixed autonomy traffic, in which we are studying the impact of a small proportion of self-driving vehicles on the rest of traffic flow. FLOW is a traffic control benchmarking framework. It provides a suite of traffic control scenarios (benchmarks), tools for designing custom traffic scenarios, and integration with deep reinforcement learning and traffic microsimulation libraries. To this day, it already includes two of them: SUMO and AIMSUN. Recently, we deployed benchmarks that are now available to the research community to benchmark their different algorithms.
Network Optimization and Analysis of the Impact of Information on Traffic Flow
This project focuses on analysis of the impact of routing apps, such as Google, Waze, Apple traffic, INRIX, etc. Our approach develops new network traffic flow models that incorporate the impact of routing information on traffic flow and routing. We provide theoretical analysis of the resulting mathematical framework, as well as numerical simulations for practical cases of the impact of such apps on congestion.