I am a Ph.D. candidate in the Department of Biological Sciences at Columbia University in New York City. My doctoral research focuses on the evolutionary processes that have shaped patterns of genetic variation in modern human populations. I develop inference methods using C/C++, Python and the Shell to test mathematical models of these evolutionary processes on huge human genomics datasets using a high performance computing cluster. Before starting my doctoral work I received an M.A. in Population Genetics from Columbia, which included coursework in statistics, probability models and data structures in C.

Prior to starting at Columbia, I completed a B.S. in Neuroscience at Brandeis University in Waltham, MA, and then worked as a research assistant for 3 years at Rockefeller University in New York City. During my time at Rockefeller I contributed to research on gene expression in early mammalian brain development, for which I was included as author on a scientific paper.

I have always enjoyed problem solving and building things, which steered me into the sciences from a young age. Although I only began programming as a graduate student, it has become a natural outlet for these interests and I deeply enjoy developing "virtual" machines to tackle different problems. My doctoral work has been the perfect setting to hone these skills: a sustained, large scale project including "big" data processing and visualization, model simulation, statistical inference and parallel computing. I've taken the opportunity to go beyond basic scripting and automation to build efficient, modular code based on design pattern principles, while simultaneously developing a robust, well-documented user interface for a software release to accompany publication of my original research.

After completing my Ph.D., I plan to continue working as a programmer focusing on data science and software engineering. Although I spent many years tackling specific problems in biological systems, learning to program has heightened my interest in the more general problem of building computational machinery for quantitative data analysis. For this reason I believe I'd enjoy working in a range of industries, provided that there are opportunities to work on interesting problems and develop professionally. Although I function well independently, I also enjoy working on a team and I get along well with all sorts of people, having worked with quite a diverse array of colleagues over the years. I'm also interested in artificial intelligence and deep learning and I plan to pursue introductory coursework in these topics at Columbia in the coming year.