Alexander Volfovsky

Alexander Volfovsky

Assistant Professor of Statistical Science


I am interested in theory and methodology for network analysis, causal inference and statistical/computational tradeoffs and in applications in the social sciences. Modern data streams frequently do not follow the traditional paradigms of n independent observations on p quantities of interest. They can include complex dependencies among the observations (e.g. interference in the study of causal effects) or among the quantities of interest (e.g. probabilities of edge formation in a network). My research is concerned with developing theory and methodological tools for approaching such modern data structures by better understanding these underlying dependence structures. My work concentrates on better understanding Kronecker covariance structures as they are related to network analysis and high dimensional unbalanced factorial designs. I work on theory and methodology for high dimensional data as it relates to network analysis, causal inference and computational and statistical tradeoffs. My primary applied interest is in the health and social sciences with past and ongoing collaborations studying friendship formation in high schools, employment outcomes for college graduates and job mobility as a function of an underlying social network.

Education & Training

  • Ph.D., University of Washington 2013

  • B.Sc. (hons), University of Chicago 2009

  • M.S., University of Chicago 2009

Selected Grants

Building Better Teams: A Network Analysis Approach awarded by (Principal Investigator). 2018 to 2021

QuBBD: Collaborative Research: Matching Methods for causal inference: big data and networks awarded by National Institutes of Health (Principal Investigator). 2017 to 2020

Meetings of New Researchers in Statistics and Probability awarded by National Science Foundation (Principal Investigator). 2019 to 2020

Theory and Methods for Community Detection with Heterogeneous Networks awarded by North Carolina State University (Co-Principal Investigator). 2019

NSF Causal Inference Workshops awarded by National Science Foundation (Principal Investigator). 2018 to 2019

Dieng, Awa, et al. “Interpretable Almost-Exact Matching for Causal Inference..” Proceedings of Machine Learning Research, vol. 89, Apr. 2019, pp. 2445–53.