Research Interests
Machine Learning / AI Method Development for Omics
We develop computational methods leveraging interpretable machine learning / AI models, large-scale single-cell genomics data, cutting-edge spatial transcriptomics, and multi-omic datatypes like spatial transcriptomics and multiomics.
Multi-Cellular Tumor Microenvironment
Tumor, like many multi-cellular disease systems, are composed of multiple cell types. For solid tumor, cancer-intrinsic properties like proliferation, mutations, and epigenetic changes, and cancer-extrinsic properties like tumor-infiltrating immune cell states and inflammation, both affect tumor progression and therapeutic responses. We aim to understand molecular gene programs of tumor microenvironment (TME) cell states, pinpoint functional cancer-TME interactions, and identity targetable tumor immunity modulators. We are core members of the Brain Tumor Omics Program at Duke Preston Robert Tisch Brain Tumor Center. We will use our computational expertise to develop methods that allow us to understand the incurable tumor types and to improve cancer therapy efficacy.
Integrating Human Genetics and Functional Genomics
Human genetic variants are natural probes to investigate cell context-dependent gene regulation related to human disease. Genome-wide association studies (GWAS) identified many genetic variants associated with cancer susceptibility. We are interested in building computational methods to find cell-dependent effect of genetic variants by integrating GWAS summary statistics and functional genomics.