About me

Welcome! I am a machine learning, deep learning and reinforcement learning researcher who strives to solve difficult problems in real world. I am also an affiliated graduate research assistant in the Machine Learning and Data Intensive Computing Lab.

Currently, I am a PhD candidate in Computer Science from the Rochester Institute of Technology and acquired my B.E. in Software Engineering from Dalian University of Technology. My academic work has been published in ICDM, KDD, ICML, Neurips, ICLR, MICCAI, ACM MM, among other outlets. I am experienced in applying RL technique to real-world perception related or involved applications like time series analysis, speech recognition, dense object detection, network compression, etc.

Nowadays, I am focusing on RL guided model compression and safety alignment for LLMs, by incorporating uncertainty related evidential theory and practice. Besides, I also spend time on LLM benchmark construction, including data collection and model evaluation. Proven capability in developing robust, real-time perception systems, designing closed-loop simulation strategies via RL, and optimizing large language models for deployment on edge constraints—highly applicable to Autonomous Vehicle (AV) tech stacks.