School of Public Health: Townsend Lab
I have been interested in understanding genetic origins and molecular evolution of life and human diseases, and finding cures to various human diseases particularly cancer and infectious diseases. My current focus is studying cancer genomics from the perspectives of molecular evolution and population genetics, to understand the process of tumorigenesis and metastasis and to identify markers for cancer diagnosis, prognosis, and therapeutics.
Extensive Research Description
Currently, as a Research Faculty at Yale, I am expanding my research endeavors to (1) uncover the genetic trajectories of tumorigenesis and metastasis to help develop therapeutics that incorporate knowledge of the molecular evolution of cancer; and (2) quantitatively evaluate the effects of protein-coding mutations on cancer cellular survival and reproduction in an effort to understand the relative contribution of mutations to tumorigenesis and progression, and to predict patient prognosis and design treatment regiments.
During my training as a PhD student in Bioinformatics at Georgia Institute of Technology, I applied molecular phylogenetics to understand genome evolution in both modern and ancestral organisms across all three domains of life.
As a Postdoctoral Associate in the Department of Biostatistics at Yale University, I initiated and participated in multiple cancer research collaborations with colleagues from different departments including Pathology, Oncology, Immunology, Pharmacology, and Genetics. I developed computational pipelines and novel software packages to understand the origins and progression of cancer. One study utilized the computational pipeline to find origins of metastasis using next-generation sequencing data and incorporating molecular evolutionary models has been published in PNAS titled ‘Early and multiple origins of metastatic lineages within primary tumors’. I have developed two software packages incorporating theories of population genetics to detect functionally important genes and genic regions, namely Model Averaged Site Selection via Poisson Random Field (MASS-PRF) and Cancer Selection Intensity using Model-Averaged-Clustering (CSI-MAC).