Welcome to the Huang Research Group!

The main goal of our 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.

Examples of our interested 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.

Positions available!

Group News!

  • Dec 2023

    We welcome three new group members: Yichong Lao (1st year graduate student from Biophysics Program), Longbang Liu (1st year graduate student from Chemistry), and Kazuya Okita (a visiting student from Osaka University, Japan)!

  • Oct 2023

    Our IGME paper is selected as a Featured Article by JCP! It efficiently models biomolecular dynamics by employing time integrations of memory kernels and avoiding numerical instability in time-dependent memory kernels.

  • Sept 2023

    Our GraphVAMPnets provides an efficient way to find CVs for self-assembly. Graph embeddings ensure structures invariant to permutations and rotations, and the VAMP theory (no detailed-balance requirement) handles insufficient sampling of dissociation transitions.  This article …

  • Sept 2023

    Our Latent space Path Clustering (LPC) method can efficiently group parallel kinetic pathways into distinct metastable path channels. It utilizes the variational autoencoder (VAE) to learn the spatial distributions of kinetic pathways and perform path …

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