I apply various theoretical and computational techniques to solve problems at the intersection of physics, chemistry, and biology. Given the complexity of biological systems, the physics and chemistry involved are often so intricate that no reliable ab initio models can be constructed. Coarse-graining and abstract models, based on physics, chemistry, and math intuitions are general required. Methods I use can be roughly categorized as: follows:

Statistical Physics

Structure-based models for constructing mean-field and/or detailed partition functions for biomolecular systems, including but not limited to iterative structure construction, lattice cluster expansion, chemical kinetics, and polymer field theory.

Numerical Simulation

Coarse-grained simulations for investigating systems which simplified theoretical models are not applicable, or for validating the concepts and results of the theoretical models. Monte Carlo (MC) and molecular dynamics (MD) simulations in lattice and continuum space are applied.

Statistical Analysis and Supervised Machine Learning

These methods are applied to biological systems that large-scale experimental datasets are available for data mining, finding key features to help design new experiments and establish new theories. Meanwhile, theoretical and computational methods can also provide key features that can be validated by applying these methods to databases.