I am an applied statistician who thinks the world is best understood through strong theory vetted by rigorous empirical testing.  While my doctoral work focused on social science phenomena, I’ve since worked at the intersection of social and networking sciences and statistics, often working against very large-scale data sets.

I also think of myself as a dynamic generalist, both in terms of the methods I’m familiar with and the domains in which I’ve applied them.  Over the last few years, I’ve applied Item Response Theory to big text data; estimated spatio-temporal models of multi-measurement patterns; and cross-categorized enterprise scale network traffic with latent variable models.

Underlying all of the above is a passion for extracting insights from large, complex, and difficult data.

I often can’t share solutions to my work-related analyses directly.  However, I like to throw up example solutions to parallel and toy problems on my GitHub.  I also post slides and links to talks when possible.