Joshua Snoke is an associate statistician at the RAND Corporation. His research focuses on increasing researchers’ access to data restricted due to privacy concerns, utilizing methods such as the creation of synthetic datasets, differentially private algorithms, and the enabling of model estimation across remote partitioned databases. In addition to privacy, he works on manpower and personnel questions, such as evaluating marketing and recruitment strategies, evaluating predictors of success, and staffing models. His work, both privacy-related and otherwise, has involved a variety of administrative datasets, such as from the U.S. Census Bureau, the UK Administrative Data Research Network, the U.S. Army Recruiting Command, and the American Educator Panels. His broader methodological interests include estimation, model selection, non-parametric modeling and classification, machine learning, and causal inference. Snoke 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.
Snoke, J., Raab, G. M., Nowok, B., Dibben, C., and Slavković, A., "General and specific utility measures for synthetic data.," Journal of the Royal Statistical Society: Series A (Statistics in Society), 181(3), 2018
Snoke, Joshua, Brick, T. R., Slavković, A., and Hunter, M. D., "Providing accurate models across private partitioned data: Secure maximum likelihood estimation.," The Annals of Applied Statistics, 12(2), 2018
Snoke, J. and Slavković, A.. "pMSE Mechanism: Differentially Private Synthetic Data with Maximal Distributional Similarity.," in , Privacy in Statistical Databases. pp. 138-159, Springer, 2018