Summary
Kevin Chang is a robotics and autonomy researcher with 12 years of hands-on experience developing learning-based control systems for legged and underwater robots. Currently a Graduate Research Assistant at Oregon State University, he focuses on learning-based controls in the Dynamic Robotics and AI Lab and brings prior work on deep reinforcement learning for AUV docking and SLAM improvements for autonomous racing. His projects have produced peer-reviewed outputs (ICRA) and industry-facing applications, from semantic segmentation models for lumber inspection to simulation-to-reality studies that quantify and reduce sim2real gaps. Comfortable spanning research and applied engineering, Kevin pairs strong experimental rigor with practical implementation skills across perception, control, and simulation. Based in Corvallis, he combines academic mentorship under noted advisors with seasonally embedded research at institutions like Woods Hole, indicating a pattern of collaborative, cross-domain problem solving. An accessible communicator who maintains a personal site and active GitHub, he often translates research prototypes into tangible system improvements for robotics teams.
12 years of coding experience
13 years of employment as a software developer
Horace Mann School
BS Finance Computer Science Information Systems, BS Finance Computer Science Information Systems at Boston College Carroll School of Management