I'm a Postdoctoral Fellow at Johns Hopkins University, supervised by Ilya Shpitser
(computer science, JHU) and Eric Tchetgen Tchetgen (statistics, UPenn). I earned my PhD from Carnegie Mellon University in December 2017. Before coming to CMU, I did my undergraduate studies at Columbia University. In July 2020, I'll be starting as Assistant Professor in the Department of Biostatistics at Columbia University's Mailman School of Public Health.
My research is situated at the intersection of machine learning and statistics, focusing on causal inference and its applications in data-driven settings, particularly public health and biomedicine. I develop statistical methods and machine learning tools based on graphical models (e.g., DAGs/Bayesian networks or related), which can support inference about treatment effects, policies, and counterfactuals. 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.
At Hopkins I'm affiliated with the Malone Center for Engineering in Healthcare. I'm also connected to the Machine Learning Group and the Causal Inference Working Group. While at CMU I was a member of the algorithm development / data science research group in the Center for Causal Discovery.
Visit the research page for a look at some of my papers and presentations.
Contact:
malinsky {at} jhu {dot} edu
(computer science, JHU) and Eric Tchetgen Tchetgen (statistics, UPenn). I earned my PhD from Carnegie Mellon University in December 2017. Before coming to CMU, I did my undergraduate studies at Columbia University. In July 2020, I'll be starting as Assistant Professor in the Department of Biostatistics at Columbia University's Mailman School of Public Health.
My research is situated at the intersection of machine learning and statistics, focusing on causal inference and its applications in data-driven settings, particularly public health and biomedicine. I develop statistical methods and machine learning tools based on graphical models (e.g., DAGs/Bayesian networks or related), which can support inference about treatment effects, policies, and counterfactuals. 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.
At Hopkins I'm affiliated with the Malone Center for Engineering in Healthcare. I'm also connected to the Machine Learning Group and the Causal Inference Working Group. While at CMU I was a member of the algorithm development / data science research group in the Center for Causal Discovery.
Visit the research page for a look at some of my papers and presentations.
Contact:
malinsky {at} jhu {dot} edu