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.
I work on Multimodal Learning, Reinforcement Learning, and Imitation Learning etc., with the goal of improving the performance, generalization, and robustness of AI systems. My work spans across robotics, autonomous driving, and multimodal large language model (LLM) reasoning.
A universal, plug-and-play framework that dynamically modulates guidance signals based on associated uncertainty, providing a simple yet effective solution for incorporating uncertainty-aware guidance across diverse machine learning domains.
A multi-modal multi-agent framework that enables agents to collaborate and share multimodal data during training while allowing inference with reduced modalities during testing, which is especially beneficial for deployment in resource-constrained environments.
A 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.
A multi-dimensional Representation Learning approach that 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.
An adaptive visual imitation learning approach that exhibits adaptability and robustness across different bowl configurations and diverse food types for robotic scooping.