Ying-Chun Lee

Hi! I'm Ying-Chun Lee, and you can also call me Thomas. I'm currently a master's student in Electrical and Computer Engineering at the University of Washington. I'm passionate about Embodied AI, robotics, and software engineering, with a focus on building systems that bring learning-based methods onto real robots.

I collaborate with the PRIOR team at Ai2 and PhD researcher Jiafei Duan on real-world robotics learning and evaluation infrastructure. I've worked hands-on with several robotic platforms including Franka FR3, SO-100/SO-101 (via LeRobot), and YAM. I've fine-tuned and deployed policies like π₀, π_0.5, π₀-FAST, ACT, SmolVLA, and Diffusion Policy on real hardware, and built end-to-end data collection pipelines and teleoperation systems using GELLO and Meta Quest 2 (via Polymetis).

During my undergraduate studies, I conducted research at the DRES Lab at the University of Illinois at Urbana-Champaign (UIUC), co-advised by Prof. Yih-Kuen Jan and Prof. Chih-Yang Lin, focusing on deep learning-based medical image segmentation from shoulder ultrasound images. This work led to a first-author publication in IEEE Access (2024).

I also interned at Chang Chun Group as a Robotics Engineer, where I built a digital twin and imitation learning platform for the ALOHA VX300S arm in NVIDIA Isaac Sim, and worked on the Husky Robotics team at UW, developing real-time perception and depth-sensing stacks for a Mars rover competing in the University Rover Challenge. This summer, I'll be joining Amazon as a Software Development Engineer Intern.