Rate My Professor Patrick Brown

Patrick Brown is an Associate Professor in the Department of Statistical Sciences at the University of Toronto, with a cross-appointment to the Centre for Global Health Research at St. Michael’s Hospital. He holds a PhD from the University of Lancaster. His research focuses on models and inference methodologies for spatio-temporal data, motivated by problems in spatial epidemiology and the environmental sciences. This work includes Bayesian inference for non-Gaussian spatial data and non-parametric methods for spatially aggregated and censored locations. Brown’s areas of interest encompass spatial statistics, cancer statistics, and statistical software. He has developed several R packages, including mapmisc for maps and projections, geostatsp for geostatistical modelling, and diseasemapping for computing observed and expected counts from case and population data.

Brown has authored or co-authored numerous peer-reviewed publications in leading statistical and applied journals. Representative works include papers on Matérn covariance and Gaussian Markov random fields in Statistical Science, multi-event dynamic capture-recapture models in Infectious Disease Modelling, and investigations into precipitation, temperature, and snakebite mortality in Spatial and Spatio-temporal Epidemiology. Additional contributions address Poisson cluster process models, anisotropic Matérn covariance functions, cellphone-derived mobility networks for COVID-19 risk assessment, wastewater surveillance using differentiable Gaussian processes, and various applications of spatial and Bayesian methods to disease mapping, air pollution epidemiology, and infectious disease modelling. His scholarship emphasizes computational efficiency, scalable approximations to posterior distributions, and practical tools for analyzing complex spatial and temporal datasets in health and environmental contexts.