Research

Our research interests lie in the interdisciplinary fields of computational biology and applied machine learning, with a strong focus on gene regulation. Our lab aims to develop sophisticated bioinformatics and machine learning methods that can be used to build integrative models of transcriptional regulation and interpret non-coding genetic variation from the perspective of gene regulation. We believe that a comprehensive understanding of gene regulation at the genome-wide and system-level is essential for uncovering the causal genetic and molecular mechanisms of disease and aging.

What distinguishes our research is our ability to extract novel insights from vast and complex genomic data using interpretable and predictive computational models. We collaborate extensively with experimental biologists within and beyond Westlake University to validate our hypotheses and discover new biology. Our current research focuses on several critical areas, including:

1. Developing scalable computational tools for single-cell genomics.
2. Constructing transcriptional regulatory networks from single-cell multi-omics data
3. Learning dynamic regulatory models by integrating functional genomic data from temporal and perturbation experiments
4. Prioritizing and interpreting functional genetic variation and its impact on chromatin, expression, and cellular phenotypes in healthy and diseased states.

(Click here to see the list of selected publications from our lab)