About Me

I am currently a Physical AI Postdoc Fellow at Purdue University, supervised by Dr. Guang Lin and Dr. Ruqi Zhang. Prior to Purdue, I was a postdoc in the Department of Computer Science at Vanderbilt University (2022-2025), supervised by Dr. Soheil Kolouri. I received my Ph.D. in Electrical and Computer Engineering from the University of Delaware in 2022, where I studied machine learning and statistics under the guidance of Dr. Dominique Guillot.

Research Interests {:#research-interests}

I am interested in developing scalable, reliable, and efficient probabilistic methods for machine learning and generative AI. My work aims to bridge theoretical foundations and practical implementations, with the goal of building trustworthy and efficient AI systems for real-world settings.

Currently, I focus on the following key directions:

1. Efficient generation and learning on discrete and continuous data using flow matching and diffusion models.
2. Reinforcement learning methods for both classical and modern generative models, including agentic RL.
3. Time-series modeling and forecasting, stochastic processes, and sequential statistical learning.
4. Uncertainty quantification, probabilistic inference, and Bayesian methods for robust decision-making.