My research involves theoretical modeling of evolutionary phenomena at the population level and development and applications of computational statistical methods to perform inference about evolutionary phenomena using population genetic data. I maintain a broad interest in statistical theory and philosophy of statistics, evolutionary theory, and complex systems.

Currently, I focus on inference under statistical models with computationally intractable likelihoods. In particular, I work on theory and applications of approximate Bayesian computation (ABC), a class of computational statistical methods to perform inference from models with computationally intractable likelihoods. I maintain a website to keep track of developments related to the ABC methods. The website is meant to be a resource both for biologists and statisticians who want to get familiar with ABC methods and contains a short introduction to ABC, meeting announcements, and a comprehensive list of publications.

I also work on topics in theoretical population genetics. My current interest in this area is in theoretical modeling and applications of admixture to reconstruct the recent and ancient history of human populations.

My collaborators include my postdoctoral advisor Noah Rosenberg at Stanford University, my PhD advisor Paul Joyce at University of Idaho, Paul Verdu at CNRS, Paul Hohenlohe at University of Idaho.