James Johndrow earned his doctorate from the statistics department at Duke University and has been a collaborator and consultant with HRDAG since 2014. James has an active research program in Bayesian methods for analysis of contingency tables, a meta-topic that encompasses many methods used in population estimation. His work with HRDAG focuses on development of novel methods for population estimation, and adaptation of existing methods for contingency tables and count data to the population estimation context, with the goal of advancing the state of the art in population estimation research and practice. His other research interests include Markov chain Monte Carlo, the most common approach to computation for complex Bayesian models. His most recent HRDAG-related work is here.

James is a Stein Fellow/Lecturer in the department of statistics at Stanford University. He received his MS in statistics from Duke in 2012, and a BA in chemistry from Amherst College in 2003. He is a statistical consultant/adviser for several technology companies. Previously, James was a consultant at NERA Economic Consulting and a research technician at UCSF and Fred Hutchinson Cancer Research Center.