Taekyung Kim is a Robotics PhD candidate at the University of Michigan with eight years of engineering and research experience building autonomy for ground vehicles and industrial vision systems. His work spans model-based MPC, sim-to-real neural dynamics, and reinforcement-learning policies for exploration and off-road driving, developed using ROS/ROS2, PyTorch, C++, and Python. He has applied these skills in both defense and industry settings—designing UGV autonomy and EOD robots at the Agency for Defense Development and delivering deep-learning inspection software in commercial roles. Taekyung combines rigorous academic training (summa cum laude undergraduate) with hands-on deployment experience in IPG CarMaker and Nvidia Isaac, and he brings a practical focus on transferring learned models from simulation to real vehicles. Notably, his background includes rapid prototyping of cooperative urban driving stacks and multi-purpose autonomous platforms, reflecting a blend of systems-level thinking and low-level control expertise.
7 years of coding experience
2 years of employment as a software developer
University of California, Berkeley
PhD, Robotics, PhD, Robotics at University of Michigan
Bachelor's degree, College of Transdisciplinary Studies, 3.92/4.0, Summa Cum Laude, Bachelor's degree, College of Transdisciplinary Studies, 3.92/4.0, Summa Cum Laude at DGIST (Daegu Gyeongbuk Institute of Science and Technology)
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