Paul Damien

Paul Damien is a professor in Business & Economics at the University of Texas at Austin, where he holds the B. M. (Mack) Rankin, Jr. Professorship in Business in the Department of Information, Risk, and Operations Management, Red McCombs School of Business. He is also a Professor in the Department of Statistics and Data Sciences. Damien joined the University of Texas at Austin in 2004 after serving on the faculty at the University of Michigan Ross School of Business. He earned his Ph.D. in Mathematics from Imperial College London in 1995. In addition, he has been a visiting scholar at the Department of Economics, University of Groningen, Netherlands, and consults for several companies in various industries worldwide.

Damien's research specializations encompass Bayesian statistics, lying at the intersection of mathematics, Bayesian statistics, and applications in business, engineering, and stochastic optimization. He is a Fellow of the Royal Statistical Society of England, a twice recipient of the United Kingdom Engineering and Physical Sciences Research Council Research Award, and recipient of the United Kingdom Overseas Visiting Fellowship awarded by the Royal Statistical Society of England. Key publications include the co-edited book Bayesian Theory and Applications (2013), Gibbs Sampling for Bayesian Non-Conjugate and Hierarchical Models by Using Auxiliary Variables (Journal of the American Statistical Association, 1999), Prior Distributions on Symmetric Groups (Psychometrika, 2025), The Impact of Renewable Energy Tax Incentives on Electricity Pricing in Texas (Applied Sciences, 2023), and Bayesian estimation of electricity price risk with a multi-factor mixture of densities (Energy Economics, 2022). With over 3,000 citations across 113 publications, his work has influenced areas such as pandemic decision-making, healthcare cost analysis, and electricity market modeling.

Professional Email: paul.damien@mccombs.utexas.edu