Overview
I am an Assistant Professor at Duke University as primary faculty in Department of Neurosurgery and secondary in Department of Biostatistics and Bioinformatics. My passion sits at the intersection of computational method development and biomedical and genomics data. I did PhD in Bioengineering at University of Illinois at Urbana-Champaign and postdoc at Dana-Farber Cancer Institute and Harvard University School of Public Health. We have been developing integrative computational genomic methods to identify functional gene regulatory mechanisms behind disease-associated human genetic variants, machine learning methods that leverage large-scale single-cell genomics data to understand cell states in tumor. My lab at Duke focuses on computational biology, bioinformatics, and machine learning in genomics. Our research interest includes developing interpretable machine learning methods for patient-based single-cell, spatial transcriptomics, and multi-omics data, and also building integrative genomics methods that combines and functional genomics, to understand multi-cellular systems like tissues and tumor microenvironment, and to finally enable translational and biomedical discoveries.
Current Appointments & Affiliations
Assistant Professor of Neurosurgery
·
2024 - Present
Neurosurgery, Neuro-Oncology,
Neurosurgery
Assistant Professor in Biostatistics & Bioinformatics
·
2024 - Present
Biostatistics & Bioinformatics, Division of Translational Biomedical,
Biostatistics & Bioinformatics
Assistant Professor of Biomedical Engineering
·
2025 - Present
Biomedical Engineering,
Pratt School of Engineering
Member of the Duke Cancer Institute
·
2024 - Present
Duke Cancer Institute,
Institutes and Centers
Recent Publications
cellSTAAR: incorporating single-cell-sequencing-based functional data to boost power in rare variant association testing of noncoding regions.
Journal Article Nat Methods · December 31, 2025 Understanding how rare genetic variants influence complex traits remains a major challenge, particularly when these variants lie in noncoding regions of the genome. The effects of variants within candidate cis-regulatory elements (cCREs) often depend on th ... Full text Link to item CitescATAnno: Automated Cell Type Annotation for Single-cell ATAC Sequencing Data.
Journal Article Genomics Proteomics Bioinformatics · November 24, 2025 Recent advances in single-cell epigenomic techniques have created a growing demand for scATAC-seq analysis. One key analysis task is to determine cell type identity based on the epigenetic data. We introduce scATAnno, a python package designed to automatic ... Full text Link to item CiteSTHD: probabilistic cell typing of single spots in whole transcriptome spatial data with high definition.
Journal Article Genome Biol · July 18, 2025 Recent advances in spatial transcriptomics technologies have enabled gene expression profiling across the transcriptome in spots with subcellular resolution, but high sparsity and dimensionality present significant computational challenges. We present STHD ... Full text Link to item CiteEducation, Training & Certifications
University of Illinois, Urbana-Champaign ·
2019
Ph.D.