I am a Ph.D. student in Computer Science at the University of Maryland, College Park, working with Prof. Pratap Tokekar and Prof. Ming Lin. Previously, I earned my bachelor’s degree from Shanghai Jiao Tong University. I have also interned at Apple and Tencent Robotics X.
My research focuses on developing robust and adaptive learning algorithms for autonomous systems. Specifically, I work on Imitation Learning, Reinforcement Learning, Multimodal Learning, and Representation Learning etc. My goal is to advance AI-driven decision-making by improving efficiency, generalization, and robustness in real-world scenarios, such as Robotics and Autonomous Driving.
A universal framework that leverages conformal prediction to quantify teacher prediction uncertainty and dynamically adjust its guidande on the student under domain shifts.
A novel multi-agent multi-modality framework that enables agents to collaborate and share multimodal data during training while allowing inference with reduced modalities per agent during testing.
A novel multi-modal collaborative decision-making approach for connected autonomy.
A thorough iteration complexity analysis for the risk-sensitive policy gradient method, focusing on the REINFORCE algorithm and employing the exponential utility function.
Integrated Multi-Dimensional Representation Learning, which integrates visual, physical, temporal, and geometric representations to enhance the robustness and generalizability of Imitation Learning for food acquisition.
Long-horizon Visual Action-based (LAVA) food acquisition of liquid, semisolid, and deformable foods.
A data-driven technique for estimating the uncertainty and bound for the KL divergence for distributionally robust optimal control.
Adaptive visual imitation learning that exhibits adaptability and robustness across different bowl configurations and diverse food types for robotic scooping.