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.
Current Appointments & Affiliations
- Assistant Professor of Statistical Science, Statistical Science, Trinity College of Arts & Sciences 2016
- Assistant Professor of Computer Science, Computer Science, Trinity College of Arts & Sciences 2020
Contact Information
- Background
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Education, Training, & Certifications
- Ph.D., University of Washington 2013
- B.Sc. (hons), The University of Chicago 2009
- M.S., The University of Chicago 2009
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Previous Appointments & Affiliations
- Scholar In Residence in the Department of Statistical Science, Statistical Science, Trinity College of Arts & Sciences 2015 - 2016
- Research
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Selected Grants
- CAREER: Design and analysis of experiments for complex social processes awarded by National Science Foundation 2021 - 2026
- RAISE:IHBEM: Equilibrium network formation and infectious-disease spread: bridging the divide between mathematical biology and economics awarded by National Science Foundation 2022 - 2025
- Designing Social Media to Promote Intellectual Humility awarded by John Templeton Foundation 2022 - 2025
- FAI: An Interpretable AI Framework for Care of Critically Ill Patients involving Matching and Decision Trees awarded by National Science Foundation 2022 - 2025
- Meetings of New Researchers in Statistics and Probability awarded by National Science Foundation 2020 - 2024
- PIPP Phase I awarded by National Science Foundation 2022 - 2024
- HDR TRIPODS: Innovations in Data Science: Integrating Stochastic Modeling, Data Representation, and Algorithms awarded by National Science Foundation 2019 - 2023
- Meetings of New Researchers in Statistics and Probability awarded by National Science Foundation 2019 - 2022
- Building Better Teams: A Network Analysis Approach (Research Areas of Interest: 2. Leader Development, 3. Personnel Testing and Performance, and 4. Organizational Effectiveness) awarded by U.S. Army Research Inst. for Behavioral and Social Sciences 2018 - 2022
- Collaborative Research: RAPID: Statistical tools to quantify and mitigate the spread of COVID-19 awarded by National Science Foundation 2020 - 2022
- QuBBD: Collaborative Research: Matching Methods for causal inference: big data and networks awarded by National Institutes of Health 2017 - 2021
- Theory and Methods for Community Detection with Heterogeneous Networks awarded by North Carolina State University 2019
- R13 Conference Grant application for the Institute for Mathematical Statistics New Researchers Conference to be held July 26, 2018 awarded by National Institutes of Health 2018 - 2019
- NSF Causal Inference Workshops awarded by National Science Foundation 2018 - 2019
- missing activity
- missing activity
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External Relationships
- Kettering Foundation
- Wells Fargo & Co
- Publications & Artistic Works
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Selected Publications
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Academic Articles
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Bu, F., A. E. Aiello, J. Xu, and A. Volfovsky. “Likelihood-Based Inference for Partially Observed Epidemics on Dynamic Networks.” Journal of the American Statistical Association 117, no. 537 (January 1, 2022): 510–26. https://doi.org/10.1080/01621459.2020.1790376.Full Text
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Weltz, Justin, Alex Volfovsky, and Eric B. Laber. “Reinforcement Learning Methods in Public Health.” Clinical Therapeutics 44, no. 1 (January 2022): 139–54. https://doi.org/10.1016/j.clinthera.2021.11.002.Full Text
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Zivich, Paul N., Alexander Volfovsky, James Moody, and Allison E. Aiello. “Assortativity and Bias in Epidemiologic Studies of Contagious Outcomes: A Simulated Example in the Context of Vaccination.” American Journal of Epidemiology 190, no. 11 (November 2021): 2442–52. https://doi.org/10.1093/aje/kwab167.Full Text
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Hollenbach, F. M., I. Bojinov, S. Minhas, N. W. Metternich, M. D. Ward, and A. Volfovsky. “Multiple Imputation Using Gaussian Copulas.” Sociological Methods and Research 50, no. 3 (August 1, 2021): 1259–83. https://doi.org/10.1177/0049124118799381.Full Text
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Tierney, Graham, Christopher Bail, and Alexander Volfovsky. “Author Clustering and Topic Estimation for Short Texts,” June 15, 2021.Link to Item
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Ciccone, Emily J., Paul N. Zivich, Evans K. Lodge, Deanna Zhu, Elle Law, Elyse Miller, Jasmine L. Taylor, et al. “SARS-CoV-2 Infection in Health Care Personnel and Their Household Contacts at a Tertiary Academic Medical Center: Protocol for a Longitudinal Cohort Study.” Jmir Research Protocols 10, no. 4 (April 2021): e25410. https://doi.org/10.2196/25410.Full Text
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Gupta, Neha R., Vittorio Orlandi, Chia-Rui Chang, Tianyu Wang, Marco Morucci, Pritam Dey, Thomas J. Howell, et al. “dame-flame: A Python Library Providing Fast Interpretable Matching for Causal Inference,” January 5, 2021.Open Access Copy Link to Item
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Wang, T., M. Morucci, M. U. Awan, Y. Liu, S. Roy, C. Rudin, and A. Volfovsky. “FLAME: A fast large-scale almost matching exactly approach to causal inference.” Journal of Machine Learning Research 22 (January 1, 2021).Open Access Copy
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Wang, Tianyu, Marco Morucci, M Usaid Awan, Yameng Liu, Sudeepa Roy, Cynthia Rudin, and Alexander Volfovsky. “FLAME: A Fast Large-scale Almost Matching Exactly Approach to Causal Inference.” J. Mach. Learn. Res. 22 (2021): 31:1-31:1.
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Brinkley-Rubinstein, Lauren, Katherine LeMasters, Phuc Nguyen, Kathryn Nowotny, David Cloud, and Alexander Volfovsky. “The association between intersystem prison transfers and COVID-19 incidence in a state prison system.” Plos One 16, no. 8 (2021): e0256185. https://doi.org/10.1371/journal.pone.0256185.Full Text Link to Item
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Ciccone, Emily J., Paul N. Zivich, Evans K. Lodge, Deanna Zhu, Elle Law, Elyse Miller, Jasmine L. Taylor, et al. “SARS-CoV-2 Infection in Health Care Personnel and Their Household Contacts at a Tertiary Academic Medical Center: Protocol for a Longitudinal Cohort Study (Preprint),” November 17, 2020. https://doi.org/10.2196/preprints.25410.Full Text
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Bail, Christopher A., Brian Guay, Emily Maloney, Aidan Combs, D Sunshine Hillygus, Friedolin Merhout, Deen Freelon, and Alexander Volfovsky. “Assessing the Russian Internet Research Agency's impact on the political attitudes and behaviors of American Twitter users in late 2017.” Proceedings of the National Academy of Sciences of the United States of America 117, no. 1 (January 2020): 243–50. https://doi.org/10.1073/pnas.1906420116.Full Text
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Bu, F., S. Xu, K. Heller, and A. Volfovsky. “SMOGS: Social network metrics of game success.” Aistats 2019 22nd International Conference on Artificial Intelligence and Statistics, January 1, 2020.
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Jagadeesan, R., N. S. Pillai, and A. Volfovsky. “Designs for estimating the treatment effect in networks with interference.” Annals of Statistics 48, no. 2 (January 1, 2020): 679–712. https://doi.org/10.1214/18-AOS1807.Full Text
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Awan, M Usaid, Sudeepa Roy, Marco Morucci, Cynthia Rudin, Vittorio Orlandi, and Alexander Volfovsky. “Almost-Matching-Exactly for Treatment Effect Estimation under Network Interference.” International Conference on Artificial Intelligence and Statistics, Vol 108 108 (2020): 3252–61.Link to Item
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Morucci, Marco, Vittorio Orlandi, Cynthia Rudin, Sudeepa Roy, and Alexander Volfovsky. “Adaptive Hyper-box Matching for Interpretable Individualized Treatment Effect Estimation.” Conference on Uncertainty in Artificial Intelligence (Uai 2020) 124 (2020): 1089–98.Open Access Copy Link to Item
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Mathews, H., V. Mayya, A. Volfovsky, and G. Reeves. “Gaussian Mixture Models for Stochastic Block Models with Non-Vanishing Noise.” 2019 Ieee 8th International Workshop on Computational Advances in Multi Sensor Adaptive Processing, Camsap 2019 Proceedings, December 1, 2019, 699–703. https://doi.org/10.1109/CAMSAP45676.2019.9022612.Full Text
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Reeves, G., V. Mayya, and A. Volfovsky. “The Geometry of Community Detection via the MMSE Matrix.” Ieee International Symposium on Information Theory Proceedings 2019-July (July 1, 2019): 400–404. https://doi.org/10.1109/ISIT.2019.8849594.Full Text
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Dieng, Awa, Yameng Liu, Sudeepa Roy, Cynthia Rudin, and Alexander Volfovsky. “Interpretable Almost-Exact Matching for Causal Inference.” Proceedings of Machine Learning Research 89 (April 2019): 2445–53.
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Usaid Awan, M., Y. Liu, M. Morucci, S. Roy, C. Rudin, and A. Volfovsky. “Interpretable almost-matching-exactly with instrumental variables.” 35th Conference on Uncertainty in Artificial Intelligence, Uai 2019, January 1, 2019.
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Parikh, Harsh, Cynthia Rudin, and Alexander Volfovsky. “MALTS: Matching After Learning to Stretch.” Journal.Of.Machine.Learning.Research 23(240) (2022) 1 42, November 18, 2018.Link to Item
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Bail, Christopher A., Lisa P. Argyle, Taylor W. Brown, John P. Bumpus, Haohan Chen, MB Fallin Hunzaker, Jaemin Lee, Marcus Mann, Friedolin Merhout, and Alexander Volfovsky. “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, no. 37 (September 2018): 9216–21. https://doi.org/10.1073/pnas.1804840115.Full Text Open Access Copy
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Liu, Yameng, Aw Dieng, Sudeepa Roy, Cynthia Rudin, and Alexander Volfovsky. “Interpretable Almost Matching Exactly for Causal Inference,” June 18, 2018.Link to Item
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Toulis, Panos, Alexander Volfovsky, and Edoardo M. Airoldi. “Propensity score methodology in the presence of network entanglement between treatments,” January 22, 2018.Link to Item
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Volfovsky, A., and E. M. Airoldi. “Sharp total variation bounds for finitely exchangeable arrays.” Statistics and Probability Letters 114 (July 1, 2016): 54–59. https://doi.org/10.1016/j.spl.2016.02.013.Full Text Open Access Copy
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Basse, Guillaume W., Alexander Volfovsky, and Edoardo M. Airoldi. “Observational studies with unknown time of treatment,” January 15, 2016.Link to Item
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Sussman, Daniel L., Alexander Volfovsky, and Edoardo M. Airoldi. “Analyzing statistical and computational tradeoffs of estimation procedures,” June 25, 2015.Link to Item
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Volfovsky, Alexander, Edoardo M. Airoldi, and Donald B. Rubin. “Causal inference for ordinal outcomes,” January 6, 2015.Link to Item
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Volfovsky, Alexander, and Peter D. Hoff. “Testing for nodal dependence in relational data matrices.” Journal of the American Statistical Association 110, no. 511 (January 2015): 1037–46. https://doi.org/10.1080/01621459.2014.965777.Full Text
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Volfovsky, Alexander, and Peter D. Hoff. “HIERARCHICAL ARRAY PRIORS FOR ANOVA DECOMPOSITIONS OF CROSS-CLASSIFIED DATA.” The Annals of Applied Statistics 8, no. 1 (March 2014): 19–47. https://doi.org/10.1214/13-aoas685.Full Text
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Hoff, Peter, Bailey Fosdick, Alex Volfovsky, and Katherine Stovel. “Likelihoods for fixed rank nomination networks.” Network Science (Cambridge University Press) 1, no. 3 (December 2013): 253–77. https://doi.org/10.1017/nws.2013.17.Full Text
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Volfovsky, Alex, Katherine Stovel, Bailey Fosdick, and Peter Hoff. “Likelihoods for fixed rank nomination networks.” Network Science 1, no. 03 (December 2013): 253–77.
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Book Sections
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Ogburn, E. L., and A. Volfovsky. “Networks.” In Handbook of Big Data, 171–90, 2016.
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Conference Papers
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Awan, M Usaid, Marco Morucci, Vittorio Orlandi, Sudeepa Roy, Cynthia Rudin, and Alexander Volfovsky. “Almost-Matching-Exactly for Treatment Effect Estimation under Network Interference.” In Aistats, edited by Silvia Chiappa and Roberto Calandra, 108:3252–62. PMLR, 2020.
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Morucci, Marco, Vittorio Orlandi, Sudeepa Roy, Cynthia Rudin, and Alexander Volfovsky. “Adaptive Hyper-box Matching for Interpretable Individualized Treatment Effect Estimation.” In Uai, edited by Ryan P. Adams and Vibhav Gogate, 124:1089–98. AUAI Press, 2020.
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Bu, Fan, Sonia Xu, Katherine Heller, and Alexander Volfovsky. “SMOGS: Social Network Metrics of Game Success.” In Aistats, edited by Kamalika Chaudhuri and Masashi Sugiyama, 89:2406–14. PMLR, 2019.
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Usaid Awan, M., Y. Liu, M. Morucci, S. Roy, C. Rudin, and A. Volfovsky. “Interpretable almost-matching-exactly with instrumental variables.” In 35th Conference on Uncertainty in Artificial Intelligence, Uai 2019, 2019.
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Preprints
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Morucci, Marco, Cynthia Rudin, and Alexander Volfovsky. “Matched Machine Learning: A Generalized Framework for Treatment Effect Inference With Learned Metrics,” April 3, 2023.Link to Item
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Combs, Aidan, Graham Tierney, Fatima Alqabandi, Devin Cornell, Gabriel Varela, Andrés Castro Araújo, Lisa Argyle, Christopher A. Bail, and Alexander Volfovsky. “Perceived Gender and Political Persuasion: A Social Media Field Experiment during the 2020 Democratic National Primary.” Center for Open Science, October 5, 2022. https://doi.org/10.31235/osf.io/537qn.Full Text
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Combs, Aidan, Graham Tierney, Brian Guay, Friedolin Merhout, Christopher A. Bail, D Sunshine Hillygus, and Alexander Volfovsky. “Anonymous Cross-Party Conversations Can Decrease Political Polarization: A Field Experiment on a Mobile Chat Platform.” Center for Open Science, September 23, 2022. https://doi.org/10.31235/osf.io/cwgu5.Full Text
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Seale-Carlisle, Travis Morgan, Cynthia Rudin, Brandon Louis Garrett, Alexander Volfovsky, and Saksham Jain. “Evaluating pre-trial programs using machine learning matching algorithms.” Center for Open Science, June 13, 2022. https://doi.org/10.31234/osf.io/w4ahp.Full Text
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Bail, Christopher A., Lisa Argyle, Taylor Brown, John Bumpus, Haohan Chen, MB Fallin Hunzaker, Jaemin Lee, Marcus Mann, Friedolin Merhout, and Alexander Volfovsky. “Exposure to Opposing Views can Increase Political Polarization: Evidence from a Large-Scale Field Experiment on Social Media.” Center for Open Science, March 21, 2018. https://doi.org/10.31235/osf.io/4ygux.Full Text
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- Teaching & Mentoring
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Recent Courses
- MATH 343: Theory and Methods of Statistical Learning and Inference 2023
- STA 432: Theory and Methods of Statistical Learning and Inference 2023
- STA 493: Research Independent Study 2023
- STA 693: Research Independent Study 2023
- STA 891: Topics for Preliminary Exam Preparation in Statistical Science 2023
- MATH 343: Theory and Methods of Statistical Learning and Inference 2022
- STA 360L: Bayesian Inference and Modern Statistical Methods 2022
- STA 432: Theory and Methods of Statistical Learning and Inference 2022
- STA 790-1: Special Topics in Statistics 2022
- STA 693: Research Independent Study 2021
- STA 994: Independent Study 2021
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