Abolfazl Lavaei, Dr.-Ing.
 

Abolfazl Lavaei 

Abolfazl Lavaei
Postdoctoral Associate

Institute for Dynamic Systems and Control
ETH Zurich

OML K 32.3
OSonneggstrasse 3
8092 Zurich, Switzerland

Tel:  +41 44 632 24 42
Fax: +41 44 632 11 39

Email:   lavaei@tum.de
Web:     www.lavaei.de


Biography

Abolfazl Lavaei is a Postdoctoral Research Fellow in the Chair of Software and Computational Systems, Department of Computer Science at the Ludwig Maximilian University of Munich (LMU) since November 2019. He is also with the Hybrid Control Systems (HyConSys) Lab leading by Prof. Dr. Majid Zamani. He received the Ph.D. degree in Electrical Engineering from the Technical University of Munich (TUM), in 2019. During his Ph.D. studies, he was selected as one of the top three finalists for the IFAC Young Author Award at the 15th IFAC Symposium on Large-Scale Complex Systems: Theory and Applications (LSS), 2019. He has also received “Best Demo/Poster Award” for developing an advanced software tool in C++/OpenCL, called AMYTISS, at the 23rd ACM International Conference on Hybrid Systems: Computation and Control (HSCC), 2020. He obtained the M.Sc. degree in Aerospace Engineering with specialization in Flight Dynamics and Control from the University of Tehran (UT). For his Master's work, he has received the Best Graduate Student Award in all fields of study in Faculty of New Sciences and Technologies at the University of Tehran. He is the first & only graduate student nationwide who managed to receive a two-year Master of Science degree in two semesters (one academic-year) with the full GPA (20/20). He is also the recipient of several prestigious Ph.D. scholarships from different top-ranked universities. He is an alumni scholar of Munich Aerospace Research Group as well as DLR Graduate Program.

His line of research focuses mainly on theoretical and practical aspects of “automated (AI-based) verification and control of large-scale stochastic cyber-physical systems” with application to autonomous vehicles. His research interests revolve around the intersection of Control Theory, Optimization, Machine Learning, Artificial Intelligence, and Data Science, for which he employs compositional formal methods, reinforcement learning/deep learning, data-driven optimization, and advanced (parallel) programming in C++/Python/OpenCL.