Harrison Quick, PhD, MS
Education
Summary
My research focuses on Bayesian methods for the analysis of spatial and spatiotemporal data and applications in data privacy. Much of my research involves collaborating with experts from federal, state, and local health departments to investigate geographic and racial/ethnic disparities in public health outcomes and explore ways to expand access to public health data while preserving the privacy of the underlying data subjects. This work also permeates into my teaching interests, where I enjoy introducing concepts of Bayesian inference and spatial statistics to students with a broad range of statistical backgrounds.
Expertise
Awards and Recognition
- Outstanding Mentor Award (2020), SPH Mentor Program, University of Minnesota School of Public Health