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.

Specialties: Statistical Genetics, Machine Learning, Biostatistics, Computational Biology.