I am a Statistician at Mathematica, based in Washington, DC. My work involves directing research projects and statistical tasks across a wide range of policy applications, including Medicare/Medicaid policy evaluation, technical assistance for Medicare/Medicaid programs, education policy, child welfare, military mental health, and financial inclusion, in both domestic and international settings.
My methodological expertise includes causal inference (including propensity score matching and weighting methods, and nonparametric alternatives), Bayesian methods (hierarchical modeling, multilevel regression with poststratification, meta-analysis), machine learning and predictive modeling, analysis of longitudinal/panel/clustered data, survival analysis, functional data analysis, semiparametric regression and smoothing.
I received my PhD in Biostatistics in 2015 from the Johns Hopkins School of Public Health in Baltimore, MD, under the advisement of Ciprian Crainiceanu. My primary research interests are in developing statistical methods for real data applications with complex data structures, including longitudinal, high-dimensional, and functional data. See my Research page for more details.
I received my Masters in Public Health from the Johns Hopkins School of Public Health in 2009. I received my bachelors degrees in Biomedical/Mechanical Engineering and Computer Science from the University of Southern California.
