Personnel

Taehyun Shim, Ph.D. (Team Lead and Head of Lab)

Dr. Taehyun Shim is a professor at the University of Michigan-Dearborn and also the Lead of this lab. He did his PhD from the University of California, Davis and since then has been affiliated with University of Michigan Dearborn. Professor Shim’s research interests include mathematical modeling and simulation of dynamic systems, active vehicle safety, vehicle dynamics, control of vehicle stability, vehicle rollover, and modeling and control of hybrid electric vehicles.

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Yijun Li, Ph.D (Recent Doctoral Student/Alumni)

Dr. Yijun Li is a recent doctoral graduate (Aug 2021) in Mechanical Sciences and Engineering, who now works at Ford. He received his B.S. and M.S. degrees in Vehicle Engineering from Nanjing University of Aeronautics and Astronautics, China in 2012 and 2015, respectively. His research interests include modeling, estimation and control of vehicle dynamics systems, active safety control and autonomous driving control. He authored many conference and journal publications and also is a coauthor to two automotive industrial patents.

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Syed Adil Ahmed (Doctoral Student)

Syed Adil Ahmed is a doctoral student in Mechanical Sciences and Engineering. He completed hishis Master's in Automotive Systems Engineering from UM- Dearborn in April 2021. His focus lies in the area of Autonomous Vehicle Control Systems Design, verification and validation. Currently, Adil is working in the lab developing autonomous ADAS systems of Lane Keeping and Adaptive Cruise Control. These systems are verified first in simulation and then validated on a 1/10th scale RC car.

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Pavan Anandareddy (Recent Graduate/Alumni)

A recent graduate student and now alumni working at Rivian, Pavan pursued his Master's in Automotive systems Engineering from University of Michigan-Dearborn. Pavan had great interest in the field of Vehicle dynamics, Controls and the technologies that deals with ADAS systems. He worked worked as a Research Assistant under Professor Shim, developing the motion planning algorithm for the 1/10th scale autonomous vehicle.