I am currently a Machine Learning Research Scientist at Nuro. Before joining Nuro, I completed my Ph.D. in Electrical and Computer Engineering at Princeton University During my Ph.D., I was fortunate to work with Prof. Jaime Fernández Fisac at Safe Robotics Lab. My thesis presents algorithms designed to ensure the safety of learning-enabled autonomous systems. These algorithms are scalable to high-order dynamic systems and can handle complex deployment conditions. Additionally, they systematically unify the autonomy stack to prevent unwarranted conservativeness.

I received my B.S. in Electrical Engineering from National Taiwan University in 2019. As an undergraduate researcher in Access LAB (with Prof. An-Yeu (Andy) Wu), I focused on intelligent reconstruction for compressed sensing ECG signals. Also, I worked in the Group of Electromagnetic Applications (with Prof. Jean-Fu Kiang), focusing on direction-of-arrival estimation of signals.

Recent News

• September 2024: Our paper “Gameplay Filters: Robust Zero-Shot Safety through Adversarial Imagination” was accepted to Annual Conference on Robot Learning (CoRL) for oral presnetation.
• July 2024: Joined Nuro as a Machine Learning Research Scientist.
• June 2024: Received Hon Hai Technology Award for outstanding performance in robotics.
• May 2024: Received my Ph.D. in Electrical and Computer Engineering from Princeton University with the thesis titled Scaling Full-Stack Safety for Learning-Enabled Robot Autonomy.
• October 2023: Presented our paper “Interpretable Trajectory Prediction for Autonomous Vehicles via Counterfactual Responsibility” at IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) and gave a talk “Role of Safety: from safety-critical control to safety-informed motion forecasting” at Formal methods techniques in robotics systems: Design and control.
• September 2023: Our paper “The Safety Filter: A Unified View of Safety-Critical Control in Autonomous Systems” was accepted to Annual Review of Control, Robotics, and Autonomous Systems (preprint online).
• July 2023: Our paper “Emergent Coordination through Game-Induced Nonlinear Opinion Dynamics” was accepted to IEEE Conference on Decision and Control (CDC).
• June 2023: Our paper “Fast, Smooth, and Safe: Implicit Control Barrier Functions through Reach-Avoid Differential Dynamic Programming” was accepted to IEEE Control Systems Letters.
• May 2023: Joined Qualcomm Research- behavior planning as an engineering intern. I am fortunate to work with Pranav Desai and Stephen Chaves on integration of the autonomous vehicle software stack.
• May 2023: Our paper “Interpretable Trajectory Prediction for Autonomous Vehicles via Counterfactual Responsibility” was accepted to IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).
• March 2023: Our paper “ISAACS: Iterative Soft Adversarial Actor-Critic for Safety” was accepted to Learning for Dynamics and Control (L4DC).
• November 2022: Our paper “Sim-to-Lab-to-Real: Safe Reinforcement Learning with Shielding and Generalization Guarantees” was accepted to Special Issue on Risk-aware Autonomous Systems: Theory and Practice, Artificial Intelligence (Project Website).
• September 2022: Received a teaching assistant award from Princeton University for developing the new Intelligent Robotic Systems course. Thank you Jaime, Zixu and Duy!
• May 2022: Joined NVIDIA Research Autonomous Vehicle Group as a research intern. I am fortunate to work with Karen Leung, Yuxiao Chen, and Marco Pavone on formalizing responsibility and infusing it into the prediction and planning modules of the autonomous vehicle software stack.