Michael Li is a Ph.D. candidate in the Bazant Lab. He received his Bachelor of Science in Chemical Engineering from the University of California, Berkeley in 2020. As an undergraduate, Michael investigated polymer design for self-assembly drug delivery structures with Prof. Ting Xu. Additionally, he investigated coupled thermal-magnetic effects in fluids with guidance from Prof. Kranthi Mandadapu. His research in the Bazant Lab focuses on bettering analyses of electrochemical characterization techniques (EIS, HPPC, Pulse Trains, etc.) to tackle experimental system variance and improve physical interpretability of results. To do so, he develops new models and algorithms that hybridizes both physics-based continuum modeling with data-driven algorithms. His current systems of interest are all driven by electrochemical effects with various applications in Li-ion battery formation modeling, circuit inversion algorithms, and resistive switching memory physics. More generally, Michael is interested in the fields of thermodynamics, statistical mechanics, phase field modeling, and physics-informed algorithms.