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.

Nicole Pashley

Nicole Pashley

Statistics (PhD '20)

I'm interested in improving tools and methodology in causal inference and experimental design. Personal page.

Jameson Quinn

Jameson A. Quinn

Statistics (PhD '20)

I'm interested in approximate sampling algorithms (SMC, MCMC, and variational) for Bayesian inference, as well as in shared-outcome mechanism design (ie, democratic voting methods).

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.

Lo-Hua Yuan

Lo-Hua Yuan

Statistics (PhD '18)

My research focus is on developing statistical methods to understand causality. Much of my work addresses treatment effect heterogeneity; for example, in the context of multi-site trials with non-compliance, randomized clinical studies with longitudinal effects, or observational studies with high-dimensional covariates. I care about driving practical impact, and have advanced causal inference methods for applications in education, mental health, public policy, and two-sided online market platforms.

 

Dean Redfearn

Dean A. Redfearn

Former Lab Administrator 

I am manager of instructional technologies at HGSE and formerly lab administrator and Prof. Miratrix's assistant. I hold a PhD in political and moral philosophy from the University of Manchester in the UK; my research considered the justice and morality of children's upbringings in pluralist liberal democracies.