I am an applied statistician interested in translational genetics– particularly developing methods and systematic analysis to dissect the molecular mechanisms, prioritize gene targets, and predict disease risk of complex diseases by leveraging large-scale biobanks and multi-omics data.
I’m currently a postdoctoral research fellow in the Genetic Epidemiology and Statistical Genetics Program at Harvard T.H. Chan School of Public Health. I obtained my Ph.D. in statistics from the University of Massachusetts Amherst. During my Ph.D., I interned in the industry at Roche and Novartis. Prior to that, I was a computational scientist for 3.5 years at The Jackson Laboratory and obtained MS in Bioinformatics.
"An approximate answer to the right problem is worth a good deal more than an exact answer to an approximate problem" - John Tukey
My philosophical view is best expressed by the above quote from John Tukey. While I enjoy both data analysis and methods development, at my core, I want to be useful and contribute to impactful science more than anything else. I strive to do work motivated by problems and relevant questions from scientific collaborators.
PhD, Statistics
University of Massachusetts Amherst
Postdoc, Statistical Genetics
Harvard T.H. Chan School of Public Health
MS, Bioinformatics
Indiana University
Mathematics Fellowship2018-2019
University of Massachusetts Amherst