Abolfazl Lavaei, Dr.-Ing.
 

Abolfazl Lavaei 

Abolfazl Lavaei
Postdoctoral Research Fellow

Chair of Software and Computational Systems
Department of Computer Science
Ludwig Maximilian University of Munich (LMU)

Office: Room F U109
Oettingenstr. 67
80538 Munich, Germany

Tel:  +49 89 2180-9345
Fax: +49 89 2180-9175

Email: lavaei@lmu.de
             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 with the highest distinction, summa cum laude, 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 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 (push-button) verification and control of large-scale stochastic cyber-physical systems” with application to autonomous driving. His research interests revolve around the intersection of Control Theory, Optimization, Data Science, and Machine Learning, for which he employs compositional formal methods, reinforcement learning/deep learning, data-driven optimization, and advanced (parallel) programming in C++/Python/OpenCL.