Electrochemical energy storage, modeling, control, ML
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Focus on physics‑informed modeling and control for electrochemical energy storage. Develops reduced‑order and hybrid ML models, RL‑guided experiment design, and uncertainty quantification with HIL validation for diagnostics, parameter identification, and thermal management.
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