The main goal of the Huang Lab is to understand and manipulate biomolecular dynamics by developing and applying novel statistical mechanics-based methods that can bridge the gap between experiments and simulations. Research areas include elucidation of functional conformational changes in gene transcription, elucidation of molecular recognition and self-assembly, development of Markov State Model and Generalized Master Equation model for biomolecular dynamics, development of Integral Equation theories for solvation, and development of deep learning methods to predict protein-ligand and protein-RNA interactions.