I'm the statistical computing consultant, as well as a researcher and lecturer, in the Department of Statistics at Berkeley. As the statistical computing consultant, I'm a member of the staff of the Statistical Computing Facility. I also serve as a computing consultant with the Berkeley Research Computing program.

My statistical expertise is in the areas of Bayesian statistics and spatial statistics with primary application to environmental and public health research. My research in recent years has focused on methodology and applied work in a variety of areas, in particular: development of the NIMBLE software for hierarchical models, prediction of past vegetation using paleoecological proxy data, Bayesian methods for global health monitoring with a focus on combining disparate sources of information, and statistical methods for the analysis of extreme weather and climate events.

Lake Louise pic

Much of this work has occurred in the context of several large collaborative projects. I am a PI and the statistical lead for the PalEON project, which builds on previous work in Bayesian spatio-temporal modeling for paleoecological applications. I am the statistics technical lead for the Department of Energy CASCADE Scientific Focus Area at Lawrence Berkeley Lab, which focuses on evaluation, assessment, and attribution of extreme weather events in climate models. Finally, I am a co-PI of the NIMBLE project, which is building software for flexibly fitting general hierarchical models using a variety of fitting techniques.

Before coming to Berkeley, I was an assistant professor in the Biostatistics Department at Harvard School of Public Health, where I was a member of the Environmental Statistics group.

I finished my PhD in Statistics at Carnegie Mellon University in May, 2003. My previous training was in ecology, with an MS from Duke University and a BA from Carleton College. My Ph.D. research focused on nonstationary covariance structures for Gaussian process models with application to climatological and other spatial data and to nonparametric regression modelling.

A while back, I wrote up a little about my philosophy of what to post on the web about your research, which relates to both reproducible research and to marketing yourself as a researcher.

I also have a somewhat sparse personal home page.

Revised October 2016.

Christopher Paciorek