I received my PhD from the Division of Biostatistics at the University of Minnesota in 2013, where my research focused on Bayesian methods for spatial and spatiotemporal data analysis.  After completing my graduate training, I served as a postdoctoral researcher at the University of Missouri, where I developed new methods for generating synthetic data for public use under the mentorship of Dr. Scott Holan and Dr. Chris Wikle.  In particular, we applied my background in spatial data modeling to develop methods which would preserve the spatial dependence structure in the real data while simultaneously protecting the privacy of the data subjects.  Following my postdoctoral position, I joined the Small Area Analysis team in the CDC's Division of Heart Disease and Stroke Prevention, where I developed models for the analysis of multivariate spatiotemporal data.  I then joined the faculty of the Department of Epidemiology and Biostatistics in the Dornsife School of Public Health in the fall of 2016 prior to joining the University of Minnesota in 2023.

The bulk of my research remains within the realm of spatial statistics.  While at Drexel University, I collaborated on the small area analysis work for the SALURBAL project – exploring spatial patterns in life expectancy in Latin American cities – and on reports such as Close to Home: The Health of Philadelphia's Neighborhoods and the State of Cancer in Philadelphia in collaboration with colleagues at both the Urban Health Collaborative and the Philadelphia Department of Public Health.  With regard to my methodological work in spatial statistics, I received funding from the County Health Rankings & Roadmaps program in 2019 to develop methods that guard against oversmoothing in spatial models; these methods later served as the foundation for an R01 from NIH/NHLBI that I received which aims to develop, implement, and disseminate methods to help state and local health departments conduct spatial analyses.

In addition to my work in spatial statistics, I have also continued my work in data privacy.  In 2018, I was selected for an ASA/NCHS research fellowship at the National Center for Health Statistics to research methods for generating differentially private synthetic data suitable for use in the production of a "Synthetic CDC WONDER", methods which resulted in a publication in the Journal of the Royal Statistical Society, Series A.  This work then served as the foundation of my NSF CAREER award focused on the intersection of spatial statistics and differential privacy.  Since returning to the University of Minnesota in the Fall of 2023, I have leveraged the support I have gained through my CAREER award to obtain additional funding from the County Health Rankings & Roadmaps program to explore the feasibility of synthesizing vital statistics data at the census tract level for the purposes of creating measures of within-county health disparities and funding from the University of Minnesota's Data Science Initiative to develop and evaluate methods for and help launch a synthetic vital statistics data repository for Minnesota.

The thread that runs through all of my research is the development and application of Bayesian methods for public health.  The majority of this work pertains to the analysis of spatial and spatiotemporal data such as the number of heart-disease related deaths in US counties.  Because these spatially referenced datasets often consist of many small counts – particularly when the data are stratified by demographic factors such as age, race/ethnicity, and gender – they are often subject to various privacy protections to prevent the underlying data subjects from being identified by ill-intentioned data users.  To help combat these issues – and thereby ensure that important public health databases such as CDC WONDER remain publicly available – I have begun researching novel approaches to generate synthetic data with provable privacy protections that yield inference comparable to the true, potentially sensitive data.

• Quick, H. (2022). “Improving the utility of Poisson-distributed, differentially private synthetic data via prior predictive truncation with an application to CDC WONDER.” Journal of Survey Statistics and Methodology, 10, 596-617. [jssam]

• Quick, H. (2021). "Generating Poisson-distributed differentially private synthetic data" J. Roy. Statist. Soc., Ser. A (Statistics in Society), 184, 1093-1108. [jrss-a]

• Quick, H., Song, G., and Tabb, L.P. (2021). "Evaluating the informativeness of the Besag-York-Mollie CAR model." Spatial Spatio-temporal Epidemiol., 37, 100420. [sste]

• Quick, H., Holan, S.H., and Wikle, C.K. (2018). “Generating partially synthetic geocoded public use data with decreased disclosure risk using differential smoothing.” J. Roy. Statist. Soc., Ser. A (Statistics in Society), 181, 649-661. [jrss-a]

• Quick, H., Waller, L.A., and Casper, M. (2018). “A multivariate space-time model for analyzing county-level heart disease death rates by race and sex.” J. Roy. Statist. Soc., Ser. C (Applied Statistics), 67, 291-304. [jrss-c]

• Quick, H., Waller, L.A., and Casper, M. (2017). “Multivariate spatiotemporal modeling of age-specific stroke mortality.” Ann. Appl. Stat, 11, 2170-2182. [aoas]

• Quick, H., Holan, S.H., Wikle, C.K., and Reiter, J.P. (2015). “Bayesian marked point process modeling for generating fully synthetic public use data with point-referenced geography.” Spatial Statistics, 14, 439-451. [spasta]

• Quick, H., Carlin, B.P., and Banerjee, S. (2015).  “Heteroscedastic conditional auto-regression models for areally referenced temporal processes for analysing California asthma hospitalization data.” J. Roy. Statist. Soc., Ser. C (Applied Statistics), 64, 799-813. [jrss-c]

• Quick, H., Banerjee, S., and Carlin, B.P. (2015).  “Bayesian modeling and analysis for gradients in spatiotemporal processes.” Biometrics, 71, 575-584. [biom]

• Quick, H., Banerjee, S., and Carlin, B.P. (2013). “Modeling temporal gradients in regionally aggregated California asthma hospitalization data.” Ann. Appl. Stat, 7, 154-176. [aoas]

I enjoy mentoring students and teaching introductory courses on Bayesian inference and spatial statistics.  To that end, I have taught Applied Bayesian Analysis, Introduction to Spatial Statistics, and Research Skills in Biostatistics I-III in the Department of Epidemiology and Biostatistics at Drexel University and a one-week summer shortcourse, Introduction to Bayesian Analysis for Public and Urban Health, in conjunction with the Urban Health Collaborative's Summer Institute at Drexel University.