Risk-Weighted Data Synthesizers for Microdata Dissemination

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Notes on contributors

Jingchen (Monika) Hu

Jingchen (Monika) Hu is an assistant professor of mathematics and statistics at Vassar College. Her research focuses on data synthesis for privacy protection, including methods of Bayesian data synthesis, disclosure risk measure and evaluation, and data synthesis under differential privacy. She teaches a data confidentiality course at Vassar, where students learn about Bayesian data synthesis and differential privacy topics, as well as utility and risk evaluation methods, and complete an applied project.

Terrance Savitsky

Terrance Savitsky is a research mathematical statistician at the U.S. Bureau of Labor Statistics. One primary area of his research focuses on Bayesian model estimation under complex sampling to achieve frequentist consistency and correct uncertainty quantification of parameters with respect to the joint distribution over population generating and complex sampling. Another primary area of his research develops flexible, non-parametric models for microdata synthesis and innovates new mechanisms for these models to provide a formal differential privacy guarantee with high utility. A third major area devises Bayesian non-parametric functional data models for small area estimation and to perform inference on latent states that generate time and spatially-indexed data, including Gaussian processes and adaptive auto-regressive priors.

Matthew Williams

Matthew Williams is a mathematical statistician at the National Center for Science and Engineering Statistics (NCSES) at the National Science Foundation (NSF). He works with teams at NCSES and NSF to support the generation and use of evidence for decision-making. His current research interests include combining Bayesian and survey sampling methods, disclosure protection methods, blending survey and non-survey data, and interactive data visualization and dissemination.