Research Interest

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.

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