I'm an Assistant Professor of Biostatistics in the Mailman School of Public Health at Columbia University.
My research focuses mostly on causal inference: developing statistical methods and machine learning tools to support inference about treatment effects, interventions, and policies. I often take a perspective informed by graphical models (e.g., DAGs/Bayesian networks or related). Current research topics include structure learning (a.k.a. causal discovery), semiparametric inference, time series analysis, and missing data. I also work on algorithmic fairness: understanding and counteracting the biases introduced by data science tools deployed in socially-impactful settings. Finally, I have interests in the philosophy of science and the foundations of statistics.
Previously, I was a Postdoctoral Fellow at Johns Hopkins University. I completed my PhD at Carnegie Mellon University in 2017 and earned my BA from Columbia University in 2011.
Visit the research page for a look at some of my papers and presentations.
Contact:
dsm2128 {at} cumc.columbia.edu
My research focuses mostly on causal inference: developing statistical methods and machine learning tools to support inference about treatment effects, interventions, and policies. I often take a perspective informed by graphical models (e.g., DAGs/Bayesian networks or related). Current research topics include structure learning (a.k.a. causal discovery), semiparametric inference, time series analysis, and missing data. I also work on algorithmic fairness: understanding and counteracting the biases introduced by data science tools deployed in socially-impactful settings. Finally, I have interests in the philosophy of science and the foundations of statistics.
Previously, I was a Postdoctoral Fellow at Johns Hopkins University. I completed my PhD at Carnegie Mellon University in 2017 and earned my BA from Columbia University in 2011.
Visit the research page for a look at some of my papers and presentations.
Contact:
dsm2128 {at} cumc.columbia.edu