My research group develops molecular simulation and data-driven approaches to predict how complex materials behave under real-world conditions. We focus on phase transitions, crystallization, and nonequilibrium processes that govern stability, cohesion, and structural evolution in soft and particulate systems. A key objective is to translate molecular-level understanding into predictive tools that inform formulation design, processing, and storage. By combining physics-based modeling with machine learning, we aim to anticipate how environmental factors affect material performance. These approaches are broadly applicable across industries, including food and agriculture, pharmaceuticals, and petroleum, where controlling phase behavior, stability, and transport properties is critical.