I’m a 1st-year Robotics Ph.D. student in The Borg Lab and the Structured Techniques for Algorithmic Robotics (STAR) Lab at Georgia Tech’s School of Interactive Computing, co-advised by Prof. Frank Dellaert and Prof. Harish Ravichandar. My research interests include hierarchical robot skill learning, probabilistic graphical models for perception and control, and dexterous manipulation. I aim to understand how hierarchical architectures, used as explicit inductive biases, can enable both multi-robot systems and high-DoF manipulators to achieve greater data efficiency and inference robustness.

I earned an M.S. in Robotic Systems Development from Carnegie Mellon University in 2024. In the summer of 2025, I was a Research Scientist Intern at Meta Reality Labs in Pittsburgh, where I worked on the Codec Avatar team, focusing on 3D geometric vision, camera auto-calibration, and nonlinear optimization. At CMU, I worked in the Robot Perception Lab under Prof. Michael Kaess, where my research centered on three areas: (1) multi-robot LiDAR-based SLAM, (2) 6-DoF in-hand object pose estimation using contact sensing and proprioception, and (3) human-to-dexterous-robot hand retargeting (Manus glove to LEAP Hand).

For my master’s capstone project, I helped develop a search-and-rescue quadruped robot capable of navigating narrow, cluttered environments by integrating a LiDAR–Inertial Odometry (LIO) pipeline with Nonlinear Model Predictive Control (NMPC).

I completed my B.S. in Electrical Engineering at National Taiwan University (NTU) in 2021. During my undergraduate years, I worked as a Robotics Software Engineer at Nexuni, developing kiosk backend software and building sensor fusion pipelines for mobile robots. I also participated in the Google Hardware Product Sprint in 2021, where I worked on embedded systems and rapid prototyping.