Ph.D., 2006, Harvard University
B.S., 1999, Yale University
My primary area of interest is in semi-parametric efficiency theory with application to causal inference, missing data problems, statistical genetics and mixed model theory. In general, I work on the development of statistical and epidemiologic methods that make efficient use of the information in data collected by scientific investigators, while avoiding unnecessary assumptions about the underlying data generating mechanism.
This course will cover statistical methods for the design and analysis of observational studies. Topics will include the potential outcomes framework for causal inference; randomized experiments; matching and propensity score methods for controlling confounding in observational studies; tests of hidden bias; sensitivity analysis; and instrumental variables.
STAT962 - ADV METHODS APPLIED STAT
This course is designed for Ph.D. students in statistics and will cover various advanced methods and models that are useful in applied statistics. Topics for the course will include missing data, measurement error, nonlinear and generalized linear regression models, survival analysis, experimental design, longitudinal studies, building R packages and reproducible research.
STAT991 - SEM IN ADV APPL OF STAT
This seminar will be taken by doctoral candidates after the completion of most of their coursework. Topics vary from year to year and are chosen from advance probability, statistical inference, robust methods, and decision theory with principal emphasis on applications.
Co-winner of the Society of Epidemiologic Research and American Journal of Epidemiology Article of the Year, 2014 Description
For the paper, “Assessment and indirect adjustment for confounding by smoking in cohort studies using relative hazards model” with David Richardson, Steve Cole
and Dominique Laurier.
Career Incubator Award, Harvard School of Public Health, 2013-2014
Co-winner of the Kenneth Rothman Epidemiology Prize, 2011 Description
For the paper, “The use of negative controls to detect confounding and other sources of error in experimental and observational science.” with Marc Lipsitch and Ted Cohen.
Best Poster Award: Gene Environment Initiative Symposium, Boston, MA, 2008
Yerby Fellowship, Harvard School of Public Health, 2006-2008