I am a computer scientist who is interested in developing novel computational techniques for analyzing large data sets in order to understand biological phenomena and processes, with particular emphasis on cancer. My work draws upon methodologies from combinatorial optimization, statistical inference, and machine learning. 

I graduated in May 2018 from the Computer Science Department at Rice University and am currently a postdoc at the same institute but with a slightly different research focus. My PhD and postdoctoral advisor is Dr. Luay Nakhleh. During my postdoc, I collaborate extensively with Dr. Nicholas Navin on developing machine learning tools to decipher cancer cell evolution. I also gained extensive experience in analyzing Next Generation Sequencing data through the collaboration with Dr. Ken Chen in MD Anderson during my PhD. 

My research focuses on developing machine learning tools to 

• detect structural variations in DNA using (the combination of) a variety of sequencing technologies, including Illumina, PacBio, Oxford Nanopore, Optical Maps and 10x etc;

• analyze single cell sequencing and deconvolve cancer cell heterogeneity;

I developed several structural variation detection/characterization tools including HySA, OMIndel and BreakDown. I also participated in developing genomic tools such as BreakDancer, TIGRA-SV, CREST, novoBreak, BreakpointSurveyor and GMT Modeling. 

During 2015-2018, I was a member of Human Genome Structural Variation Consortium (HGSVC), where I took the leadership in developing and applying machine learning tools to newly sequenced data.

I also had extended experience in single-cell DNA sequencing analysis and wrote a review paper on copy number aberration detection. More details can be found in the Publication session in the Vitae tab.