Research Interests
Develop reinforcement learning, generative modeling, and probabilistic machine learning methods for high-dimensional, spatio-temporal, and sequential data (images, profiles, signals, event sequences) in industrial, energy, transportation, and healthcare systems, with a focus on anomaly detection, prognostics, and decision making. Recent directions include distributional and flow-based reinforcement learning, flow-matching and diffusion models for anomaly detection and surrogate modeling, causal structure (DAG) learning, Bayesian and physics-informed learning, and tensor methods.
- Reinforcement Learning
- Generative Models
- Anomaly Detection
- Deep Learning
- Data Science
- Artificial Intelligence
Education
- Ph.D. in Industrial Engineering, 2017, Georgia Institute of Technology
- M.S. in Computational Science and Engineering, 2016, Georgia Institute of Technology
- M.S. in Statistics, 2015, Georgia Institute of Technology
- B.S. in Economics, 2011, Peking University
- B.S. in Physics, 2011, Peking University