Bio

Brad Carlin is an experienced statistical methodologist, data analyst, technical writer, and instructor. He spent his undergraduate years at the University of Nebraska, where he majored in mathematics and actuarial science. After interning as an actuarial trainee and gaining associateship in the Society of Actuaries, he attended graduate school at the University of Connecticut, working under the supervision of Prof. Alan Gelfand. His PhD dissertation concerned adjustment to the standard ''naive'' empirical Bayes confidence intervals so that they would have the correct frequentist coverage. Following two years as a visiting assistant professor of statistics at Carnegie Mellon University, Carlin moved to the Division of Biostatistics at the University of Minnesota School of Public Health. Here he spent 27 years on the faculty, serving as division head for 7 of those years (2010-2017). His research career has featured three distinct phases. His earliest work concerned Markov chain Monte Carlo (MCMC) methods for Bayesian computing. He then moved into Bayesian methods for spatio-temporal analysis, and ultimately into Bayesian adaptive methods for clinical trial design. He has published more than 175 papers in refereed books and journals, as well as co-authored three popular textbooks: “Bayesian Methods for Data Analysis” with Tom Louis, “Hierarchical Modeling and Analysis for Spatial Data” with Sudipto Banerjee and Alan Gelfand, and ''Bayesian Adaptive Methods for Clinical Trials'' with Scott Berry, J. Jack Lee, and Peter Muller. From 2006-2009 he was editor-in-chief of Bayesian Analysis, the official journal of the International Society for Bayesian Analysis (ISBA). He has served as primary dissertation adviser for 20 PhD students to date. He also has extensive experience teaching short courses and tutorials, and previously won both teaching and mentoring awards from the University of Minnesota. See also https://www.counterpointstat.com/who-we-are.html