People

My research explores how interfacial structure, electrostatics, and solvation influence spectroscopic, electronic, and reactive behavior at aqueous interfaces. I am also interested in developing machine-learning approaches for electronic states beyond the ground state.

My research focuses on free energy calculations for protein-ligand systems, with the goal of improving the accuracy and scalability of binding thermodynamics predictions. I develop scalable estimators of potential energy landscapes to support efficient physics-based virtual screening at scale.

My thesis research focuses on developing advanced many-body polarizable force fields (MB-UCB) for biomolecules using energy decomposition analysis (EDA), as well as studying the bio-molecular interactions. I’m also involved in a drug-discovery project where we use deep reinforcement learning to generate viral inhibitors and apply physics-based methods including docking, molecular dynamics and free energy perturbation to evaluate the potency.

My research covers most aspects of computational drug discovery, including de novo generation method development, synthesizability control, and lead optimization via machine learning models. Current interest includes exploration of cofolding models and LLM for drug design.