Joshua Snoke

Statistician

Pittsburgh Office

Education

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

One Page Bio

Overview

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 the Department of Defense (DoD) and healthcare. He also works on evaluating programs and policies for workforce development. He focuses on DoD and DHS 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.

Research Focus

Recent Projects

Building a Validation Server for Administrative Tax Data
Understanding Suicide Risk Among Soldiers
Developing a Toolkit for Detecting and Correcting for Biases in Healthcare Algorithms
Reducing Demographic Disparities in Force Development
Equity Awareness in Data Privacy

Selected Publications

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

Bowen, C. M. 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. and C. M. Bowen, "How Statisticians Should Grapple with Privacy in a Changing Data Landscape," CHANCE, 33(4), 2020

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

Snoke, J. and A. Slavković, "pMSE Mechanism: Differentially Private Synthetic Data with Maximal Distributional Similarity.," Privacy in Statistical Databases, 2018

Robson, Sean, Maria C. Lytell, Matthew Walsh, Kimberly Curry Hall, Kirsten M. Keller, Vikram Kilambi, Joshua Snoke, Jonathan W. Welburn, Patrick S. Roberts, Owen Hall, and Louis T. Mariano, U.S. Air Force Enlisted Classification and Reclassification: Potential Improvements Using Machine Learning and Optimization Models, RAND Corporation (RR-A284-1), 2022

Honors & Awards

U.S. Census Bureau Dissertation Fellowship
National Science Foundation Big Data Social Science Graduate Traineeship

Publications

Journal Article
A Feasibility Study of Differentially Private Summary Statistics and Regression Analyses with Evaluations on Administrative and Survey Data
This paper studies the feasibility of using differentially private (DP) methods to make certain queries while preserving privacy.
Oct 27, 2023
Report
Advancing Equitable Decisionmaking for the Department of Defense Through Fairness in Machine Learning
This report provides policymakers and developers of machine learning algorithms with a framework and tools to produce algorithms that are consistent with the U.S. Department of Defense's equity priorities.
Jun 13, 2023
Research Brief
Actions the Army Can Take to Reduce Sexual Assault, Sexual Harassment, and Gender Discrimination
This research brief highlights top-line findings from four different projects that have implications for strategies to prevent sexual harassment and sexual assault in the U.S. Army.
Oct 20, 2022
Report
Assessment and Selection for U.S. Air Force Special Warfare: Vol. 1, Defining Attributes and Designing Rater Training
Air Force Special Warfare warrants specialized assessment and selection of future operators. The authors examine the nontechnical attributes needed to conduct challenging missions and the process for selecting candidates who can succeed.
Sep 27, 2022
Report
Is Today's U.S. Air Force Fit? It Depends on How Fitness Is Measured
In this report, the authors examine metrics used to monitor the fitness of airmen, and through their analyses of fitness data, they make a case for using multiple measures to gain a more accurate assessment of the health and fitness of the force.
May 2, 2022
Report
Physical Fitness Standards to Support Readiness and Deployability: An Examination of Department of the Air Force Policies and Culture
Through analyses involving the Air Force's Tier 1 fitness assessment (FA), the authors examine whether FA components meet their intended purpose to minimize health risks and maximize readiness of airmen.
May 2, 2022
Report
U.S. Air Force Enlisted Classification and Reclassification: Potential Improvements Using Machine Learning and Optimization Models
The authors review options for improving processes for classifying and reclassifying enlisted active-duty, non–prior service airmen for initial skills training in the United States Air Force.
Mar 14, 2022
Report
American Instructional Resources Surveys: 2021 Technical Documentation and Survey Results
This technical report provides information about the sample, survey instrument, and resultant data for the American Instructional Resources Surveys administered to principals and teachers in spring 2021 via RAND's American Educator Panels.
Oct 26, 2021
Report
Understanding Subgroups Within the Los Angeles County Sheriff's Department: Community and Department Perceptions with Recommendations for Change
RAND researchers studied deputy subgroups within the Los Angeles County Sheriff’s Department (LASD) to learn about how subgroups are formed, why they exist, and whether subgroups have affected community perceptions and trust in LASD.
Sep 10, 2021
Research Brief
Characteristics of Sexual Assault and Sexual Harassment in the U.S. Army: Implications for Prevention Efforts
This research brief highlights top-line findings from two projects that have implications for strategies to prevent sexual harassment and sexual assault in the U.S. Army.
Aug 2, 2021
Report
Organizational Characteristics Associated with Risk of Sexual Assault and Sexual Harassment in the U.S. Army
Researchers extended previous analyses that produced estimates of sexual assault and harassment risk across installations and commands. The extended analyses showed variation in risk of sexual assault and sexual harassment across groups of soldiers.
Jun 18, 2021
Research Brief
Sexual Assault and Sexual Harassment in the U.S. Army: Where Cases are Highest and Why
This research summarizes estimates of sexual assault risk and sexual harassment risk across U.S. Army installations and command, showing variation in these risks across groups of soldiers.
Jun 18, 2021
Report
COVID-19 and the State of K–12 Schools: Results and Technical Documentation from the Spring 2021 American Educator Panels COVID-19 Surveys
The authors provide technical information about the sample and survey instrument, along with resultant descriptive data, for the COVID-19 surveys administered to principals and teachers via RAND's American Educator Panels in spring 2021.
May 27, 2021
Journal Article
Comparative Study of Differentially Private Synthetic Data Algorithms from the NIST PSCR Differential Privacy Synthetic Data Challenge
This paper presents an in-depth evaluation of differentially private synthetic data sets created for the National Institute of Standards and Technology's "Differential Privacy Synthetic Data Challenge."
2021
Report
COVID-19 and the State of K-12 Schools: Results and Technical Documentation from the Fall 2020 American Educator Panels COVID-19 Surveys
This report provides information about the sample, survey instrument, and data for the coronavirus disease 2019 surveys that were administered to principals and teachers in fall 2020 via the RAND Corporation's American Educator Panels.
Nov 16, 2020
Report
Mental Health Task-Shifting in Community-Based Organizations: Implementation, Impact, and Cost — Evaluation of the Connections to Care Program
In this report, RAND researchers describe the Connections to Care task-shifting model and their evaluation of how it was implemented, its intended impact on mental health and other outcomes, and how much it costs community-based organizations.
Nov 12, 2020
Journal Article
How Statisticians Should Grapple with Privacy in a Changing Data Landscape
This article discuss the need for statisticians to become better-acquainted with disclosure avoidance methodologies, both traditional approaches and new.
2020