CV

Ying-Chun Lee

thomas910829@gmail.com
Seattle, WA, US

Summary

ECE master's student at the University of Washington working on robot learning, real-robot systems, and software engineering.

Education

  • M.S. in Electrical and Computer Engineering
    2027-03-01
    University of Washington
    GPA: 3.98/4.0
  • B.S. in Electrical Engineering
    2024-06-01
    Yuan Ze University
    GPA: 3.73/4.0 (Major: 3.94/4.0)

Work Experience

  • Software Development Engineer Intern
    2026-06-01 - 2026-09-04
    Amazon
    Software development engineering in Seattle
  • Research Collaboration
    2025-10-01 - 2026-05-01
    Research collaboration with Jiafei Duan and collaborators from Ai2 PRIOR
    Real-world robotics learning and evaluation infrastructure
  • Robotics Software Engineer
    2025-09-01 - 2026-04-01
    Husky Robotics Team, University of Washington
    Real-time perception stack for autonomous Mars rover
  • Robotics Software Engineer Intern
    2025-07-01 - 2025-08-31
    Chang Chun Group β€” Information Center
    ALOHA-ViperX 300S digital twin and imitation-learning platform

Skills

Relevant Coursework

  • ROS
  • Self-Driving Cars
  • Software Development
  • Embedded Systems
  • Smart Systems
  • Artificial Intelligence
  • Deep Learning
  • Computer Vision
  • Internet of Things
  • Cloud Computing

Programming

  • Python
  • C
  • C++
  • Java
  • TypeScript
  • React
  • HTML
  • CSS
  • SQL

Tools & Frameworks

  • ROS 2
  • NVIDIA Isaac Sim
  • Omniverse USD
  • PyTorch
  • OpenCV
  • NumPy
  • PIL
  • Docker
  • AWS
  • GCP

Publications

  • TOPReward: Token Probabilities as Hidden Zero-Shot Rewards for Robotics
    2026
    Conference on Robot Learning (CoRL)
  • MolmoB0T: Large-Scale Simulation Enables Zero-Shot Manipulation
    2026
    Conference on Robot Learning (CoRL)
  • MolmoAct2: Action Reasoning Models for Real-world Deployment
    2026
    Conference on Robot Learning (CoRL)
  • MolmoSpaces: Large-Scale Open Ecosystem for Robot Manipulation and Navigation
    2026
    Robotics: Science and Systems (RSS) 2026
  • Using Deep Learning-Based Methods for Automated Segmentation of Soft Tissues from Shoulder Ultrasound Images
    2024
    IEEE Access