Joshua Snoke is a statistician at RAND. He researches statistical data privacy, algorithmic fairness, and workforce development.

He has expertise broadly in statistical data privacy and has published technical papers and policy reports on topics such as synthetic datasets, model estimation across multiple databases, and differentially private algorithms. He focuses mainly on evaluating and developing practical applications of privacy-preserving methodologies to identify technologies which increase access to administrative or survey data while maintaining privacy and confidentiality.

Outside of privacy, he works on algorithmic fairness and is currently working on applying these ideas to applications in criminal justice, healthcare, and the Department of Defense (DoD). He also works on evaluating programs and policies for workforce development. He focuses on DoD manpower and personnel questions, such as evaluating recruitment and promotion strategies, evaluating predictors of success in military personnel, and evaluating barriers to diversity in the workforce. 

His work, both privacy-related and otherwise, involves a variety of administrative and survey data, such as data from the U.S. Census Bureau, the Internal Revenue Service, the UK Administrative Data Research Network, the Defense Manpower Data Center, and the American Educator Panels. His broader methodological interests include estimation, model selection, non-parametric modeling and classification, and machine learning. 

He received his Ph.D. in statistics with a graduate minor in social data analytics from the Pennsylvania State University. He received his B.S. in mathematics and economics from Wheaton College.


Ph.D. in statistics, Pennsylvania State University; B.S. in mathematics, economics, Wheaton College

Selected Work

  • Barrientos, A. F., A. R. Williams, J. Snoke, and C. M. Bowen, "A Feasibility Study of Differentially Private Summary Statistics and Regression Analyses with Evaluations on Administrative and Survey Data," Journal of the American Statistical Association, 2023
  • Snoke, Joshua, Matthew Walsh, Joshua Williams, and David Schulker, Safe Use of Machine Learning for Air Force Human Resource Management: Volume 4, Evaluation Framework and Use Cases, RAND Corporation (RR-A1745-4), 2024
  • Cabreros, Irineo, Joshua Snoke, Osonde A. Osoba, Inez Khan, and Marc N. Elliott, Advancing Equitable Decisionmaking for the Department of Defense Through Fairness in Machine Learning, RAND Corporation (RR-A1542-1), 2023
  • C. M. Bowen and J. Snoke, Do No Harm Guide: Applying Equity Awareness in Data Privacy Methods, Urban Institute, 2023
  • C. M. Bowen and J. Snoke, "Comparative Study of Differentially Private Synthetic Data Algorithms from the NIST PSCR Differential Privacy Synthetic Data Challenge," Journal of Privacy and Confidentiality, 11(1), 2021
  • Matthews, Miriam, Andrew R. Morral, Terry L. Schell, Matthew Cefalu, Joshua Snoke, and R. J. Briggs, Organizational Characteristics Associated with Risk of Sexual Assault and Sexual Harassment in the U.S. Army, RAND Corporation (RR-A1013-1), 2021
  • Snoke, J., G. M. Raab, B. Nowok, C. Dibben, and A. Slavković, "General and specific utility measures for synthetic data.," Journal of the Royal Statistical Society: Series A (Statistics in Society), 181(3), 2018
  • Snoke, Joshua, T. R. Brick, A. Slavković, and M. D. Hunter, "Providing accurate models across private partitioned data: Secure maximum likelihood estimation.," The Annals of Applied Statistics, 12(2), 2018

Authored by Joshua Snoke

  • Content Type
  • Topic
  • Region
  • Date
21 Results