Summary
Ran Wang is a research scientist and ML engineer with nine years of experience applying optimal control, reinforcement learning, and robotics to industrial and academic problems. Based in Austin, he blends PhD-level expertise in optimal control and high-dimensional robotic systems with hands-on production work—implementing control algorithms and full-stack demos for industrial AI at Rockwell Automation. His doctoral research produced an information-state based algorithm and demonstrated data-driven control (D2C) across tensegrity and fluid-interacting robots, while comparative RL work (DDPG, SAC, TD3) highlighted robustness under uncertainty. Ran’s background spans C++, MATLAB, Python, MuJoCo modeling, embedded deployment, and even rapid mobile UI prototyping, showing a rare ability to move ideas from simulation to deployed demos. He’s notable for translating advanced control theory into practical monitoring and motor-condition solutions that have progressed to pilot engagement with customers.
9 years of coding experience
5 years of employment as a software developer
Intern Student- Brain Computer Interface Lab, Electrical and Computer Engineering, Intern Student- Brain Computer Interface Lab, Electrical and Computer Engineering at University of Houston
Doctor of Philosophy, Optimal Control and Reinforcement Learning, Aerospace Engineering, 3.77 / 4.00, Doctor of Philosophy, Optimal Control and Reinforcement Learning, Aerospace Engineering, 3.77 / 4.00 at Texas A&M University
Bachelor of Science - BS, Mechanical Engineering, 3.74 / 4.00, Bachelor of Science - BS, Mechanical Engineering, 3.74 / 4.00 at Huazhong University of Science and Technology
Chinese, English