I am an associate professor in the Harvard School of Education and affiliate faculty in the Harvard Department of Statistics.  My primary research focus is on causal inference methods. In particular, much of my work is on developing methodology to assess and characterize treatment effect heterogeneity in randomized clinical trials and observational studies, and on characterizing variation in treatment impact on post-treatment or latent subgroups such as from non-compliance.  I also work on these concerns in the statistical evaluations of cluster-randomized and multi-site trials.  Other research interests include data mining using high-dimensional and sparse (regularized) methods, with a focus on text summarization and causal inference with text in contexts such as newspaper corpora, legal decisions, and databases of free-text reports.  In my work, my main concerns are usually related to the applicability, interpretability, and legitimacy of data-driven arguments, which generally leads me to examine the performance and usability of simple, minimal-assumption methods.  I received my Doctorate in Statistics from University of California, Berkeley in Spring, 2012 after switching to that field in 2009 from SESAME, a doctorate program in Mathematics and Science education also at Berkeley.  I also have a MS in Computer Science from M.I.T., a BS in Computer Science from the California Institute of Technology, and a BA in Mathematics from Reed College. Between graduate careers, I was a high school teacher and tutor for 7 years.