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.
Building Better Teams: A Network Analysis Approach awarded by U.S. Army Research Inst. for Behavioral and Social Sciences (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
R13 Conference Grant application for the Institute for Mathematical Statistics New Researchers Conference to be held July 26 , 201 8 awarded by National Institutes of Health (Principal Investigator). 2018 to 2019
NSF Causal Inference Workshops awarded by National Science Foundation (Principal Investigator). 2018 to 2019
Bail, CA, Argyle, LP, Brown, TW, Bumpus, JP, Chen, H, Hunzaker, MBF, Lee, J, Mann, M, Merhout, F, and Volfovsky, A. "Exposure to opposing views on social media can increase political polarization." Proceedings of the National Academy of Sciences of the United States of America 115.37 (September 2018): 9216-9221. Full Text Open Access Copy
Dieng, A, Liu, Y, Roy, S, Rudin, C, and Volfovsky, A. "Collapsing-Fast-Large-Almost-Matching-Exactly: A Matching Method for Causal Inference." Corr abs/1806.06802 (2018).
Roy, S, Rudin, C, Volfovsky, A, and Wang, T. "FLAME: A Fast Large-scale Almost Matching Exactly Approach to Causal Inference." CoRR abs/1707.06315 (2017). Open Access Copy
Volfovsky, A, and Airoldi, EM. "Sharp total variation bounds for finitely exchangeable arrays." Statistics & Probability Letters 114 (July 2016): 54-59. Full Text Open Access Copy
Volfovsky, A, and Hoff, PD. "Testing for nodal dependence in relational data matrices." Journal of the American Statistical Association 110.511 (January 2015): 1037-1046. Full Text
Volfovsky, A, and Hoff, PD. "HIERARCHICAL ARRAY PRIORS FOR ANOVA DECOMPOSITIONS OF CROSS-CLASSIFIED DATA." The Annals of Applied Statistics 8.1 (March 2014): 19-47. Full Text
HOFF, P, FOSDICK, B, VOLFOVSKY, A, and STOVEL, K. "Likelihoods for fixed rank nomination networks." Network Science 1.03 (December 2013): 253-277.
Parikh, H, Rudin, C, and Volfovsky, A. "MALTS: Matching After Learning to Stretch.".
Volfovsky, A, Airoldi, EM, and Rubin, DB. "Causal inference for ordinal outcomes."