Department: Materials Science & Engineering
Advisor: Lucien Brush
My work will focus on the mathematical modeling of phase‑change materials (PCMs) to advance energy‑efficient technologies. By integrating machine learning with physics‑based models, I develop tools to capture the kinetics governing PCM devices and to predict the collective behavior of interacting PCM networks. These AI-enabled, data‑driven methods will enable scalable exploration of parameter space, support high‑throughput analysis, and uncover nonlinear relationships that are difficult to capture with physics‑based models alone. The work supports low‑power, non‑volatile memory and neuromorphic computing, offering pathways to reduced energy consumption in data‑intensive systems.