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Senior Data Scientist
My primary research interest is “Cancer Treatment and Prevention through Precision Medicine” based on the analysis of genomic data of cancer patients for using genomics information to make proper clinical decisions. During my B.S in biology major, I was captivated by the fact that DNA – digital information - codes life. To gain computational skill, I joined the computational biology program for a master's degree. I learned the principle and techniques of various sequence analyses such as sequence alignment models, molecular phylogenetics, and motif searching from estimating the neural mutation rate by comparing human and mouse genomes. To have an impact on real life, I then applied my existing expertise to cancer sequencing data in order to identify neo-antigens for breast cancer vaccine development during my Ph.D. study. I learned how to analyze RNA-seq data by my own algorithm and organize/manage large data generated from the project using MySQL. As a post-doc at Stanford, I expanded my research to investigate the clinical implications of genomic features. I applied regularized regression (Elastic-net) to integrate multiple, heterogeneous genomic assays data from the Cancer Genome Atlas (TCGA) project and identify known and novel candidate drive mutations that predict tumor stage and other clinical parameters. As a research scientist, I lead a team to develop an analysis pipeline to identify clonal neoantigens for the clinical phase 1 trial of personalized immune therapy. Currently, I am building a bioinformatics pipeline to examine the landscape of T cell receptor (TCR) using single-cell sequencing data. These tools will characterize the immune phenotype in addition to clinical phenotypes.