I am Assistant Professor of Sociology and Arthur H. Scribner Bicentennial Preceptor at Princeton University where I am also affiliated with the Politics Department, the Office of Population Research, the Princeton Institute for Computational Science and Engineering, The Center for Information Technology Policy and the Center for the Digital Humanities. I develop new quantitative statistical methods for applications across computational social science. I completed my PhD in Government at Harvard in 2015 where I had the good fortune of working with the interdisciplinary group at IQSS. I also earned a master's degree in Statistics from Harvard in 2014.
I've worked extensively on methods for computer-assisted text analysis and with Justin Grimmer published an introduction to the field. Molly Roberts, Dustin Tingley and I have developed the Structural Topic Model, an unsupervised topic model geared towards inference in the social sciences. The accompanying software stm is available on CRAN and at structuraltopicmodel.com. It also includes a full vignette demonstrating its use. I've recently been working on the connections between text analysis and causal inference. Current papers cover adjusting for confounding with text, and text as treatment and outcome.
I've also written a new paper with my graduate students Ian Lundberg and Rebecca Johnson aimed at sociologists about the missing gap between theory and empirics. The paper encourages more precision in specifying the goals of quantitative work where the target of estimation is too often specified in terms of regression coefficients rather than model-free estimands.
I teach undergraduate and graduate statistics as well as the occassional course on text analysis. Course materials for these classes as well as from my summer methods camp and sociology statistics reading group are available on my teaching page.
You can find preprints of my publications on this site and on Google Scholar. Replication data for most of my work is available on my Dataverse.