Academic Credentials
  • Ph.D., Materials Science and Engineering, Seoul National University, Korea, 2019
  • B.S., Materials Science and Engineering, Seoul National University, Korea, 2013
Professional Affiliations
  • Institute of Electrical and Electronics Engineers (IEEE), 2025-Current
  • The Electrochemical Society (ECS), 2025-Current

Dr. Lee specializes in engineering of energy storage materials through data-driven methods, supporting the development of software for advanced embedded prognostic and diagnostic solutions. By leveraging his understanding of the fundamental principles of material sciences and data sciences, Dr. Lee applies machine learning (ML) to generate estimates of key battery life parameters, including state of charge (SOC), state of health (SOH), and remaining useful life (RUL).

Dr. Lee has extensive experience implementing a wide range of ML methods, including artificial neural networks (ANN), recurrent neural networks (RNN), convolutional neural networks (CNN), and physics-informed neural networks (PINN). His expertise includes data modeling and estimation techniques such as particle filters, Monte Carlo methods, and Bayesian optimization. Dr. Lee frequently works with battery reduced-order models for simulation and estimation of battery states and parameters. Additionally, he is proficient at characterizing battery materials using techniques like scanning electron microscopy with energy dispersive X-ray spectroscopy (SEM-EDS), transmission electron microscopy (TEM), and X-ray diffraction (XRD). His skillset also includes a variety of electrochemical characterization techniques, including standard battery cycling and testing, cyclic voltammetry (CV), electrochemical impedance spectroscopy (EIS), and Galvanostatic Intermittent Titration Technique (GITT).

Prior to joining Exponent, Dr. Lee was a battery engineer in the Mobile eXperience (MX) division of Samsung Electronics Co. Ltd.; his responsibilities covered the development and maintenance of on-device battery SOH estimation algorithm, on-device battery failure detection, and data-driven early assessment of long-term cycle lives of battery cells. Before working in industry, he made academic contributions in the field of third-generation photovoltaics and next-generation battery materials during his doctorate study in Seoul National University, South Korea.