PhD, Harvard University, 2011
MS, Columbia Mailman School of Public Health, 2006
My research is in causal inference and epidemiologic methods. Broadly, I am interested in developing methods for and describing the behavior of traditional statistical machinery when standard assumptions are not met. I have worked on characterizing the bias that results from misclassification, i.e. violations of the assumption that variables were measured accurately. I have also worked on semiparametric estimation of instrumental variables models, as these models are useful for certain violations of “no unmeasured confounding” assumptions. Currently, my main focus is on developing new methods for statistical and causal inference in the presence of interference (when one subject’s treatment may affect other subjects’ outcomes) and for social network data; both of these represent violations of assumptions of independence among observations.
Honors and Awards
2016 - National Academy of Sciences Kavli Fellow
2012 - Thomas R. Ten Have Award (Atlantic Causal Inference Conference)
Thomas R. Ten Have Award, Atlantic Causal Inference Conference. (Awarded for exceptionally creative
or skillful research on causal inference.)
2011 Student Research Award, Graybill Conference on Nonparametric Statistics.
2008 Robert Balentine Reed Prize for Excellence in Biostatistical Science. Department of Biostatistics, Harvard
University. (Awarded for the highest score on the doctoral qualifying exam.)
2005, 2006 Peter J. Sharp Scholarship, Columbia University. (Full-tuition, merit-based scholarship.)