Alumni

 
Zach Branson

Zach Branson

Statistics (PhD '19)

My work focuses on developing new methods for designing and analyzing experiments and observational studies. I've worked on applications in education, engineering, health, and text analysis. I'm always interested in working with other researchers and practitioners who want to make causal inferences in the face of complex experimental and quasi-experimental settings.

Luis Campos

Luis F Campos

Statistics (PhD '19)

I'm interested in understanding the principles that lead to successful model building and developing tools to guide practitioners to this end. These methodological developments and applied work have always been guided by collaborations with wonderful researchers in public health, education, political science and astrophysics.

Aaron R. Kaufman

Aaron R Kaufman

Government (PhD '19)

I work on applying machine learning, especially text, and causal inference, especially in experimental design, to American political behavior and institutions.

Reagan Rose

Reagan Mozer

Statistics (PhD '19)

My research focuses on the development and application of methods for causal inference with complex data, including text data, randomized experiments complicated by issues such as non-compliance, and observational studies. I find myself constantly drawn to applied statistics problems in a variety of fields, but areas of particular interest include public health and computational social science.

Maxime Rischard

Maxime Rischard

Statistics (PhD '19)

My research applies geostatistical methods to causal inference questions, such as the geographic regression discontinuity design (GeoRDD). More broadly, I’m interested in pushing spatial and spatiotemporal methods to new scientific questions and applications.

Lily An

Lily S An

Education (Master’s ‘18, Education Policy & Management)
 
My areas of interest include educational measurement, program evaluation, and high stakes evaluation. I care about improving communication around teacher and parent interpretations of evaluations or test scores and better understanding their uses.
Lo-Hua Yuan

Lo-Hua Yuan

Statistics (PhD '18)

I enjoy learning about causal inference methods and am especially interested in developing methodology for observational studies, understanding the legitimacy of using machine learning methods to make causal claims, and assessing treatment effect heterogeneity --- in randomized experiments as well as across subgroups defined by post-randomization behavior. Favorite application areas include education, public health, and social public policy.