Chris Hans

Associate Professor of Statistics

My main area of research interest is the development of Bayesian methodology for the analysis of modern, complex datasets. Data analysts are using increasingly complex models to address increasingly complex data at increasing scale.  My research addresses both methodological and computational aspects of statistical modeling that help advance our understanding of data analysis in these areas. From a methodological perspective, much of my recent research has focused on the development and study of prior distributions in Bayesian modeling and their impact on posterior inference. A particular focus of my work has been on the development of classes of prior distributions in regression that have connections to penalized optimization procedures. From a computational perspective, my research interests have focused on Markov chain Monte Carlo and parallel computing methods in computational statistics. Specific methodological research areas include the problems of model uncertainty and averaging in contexts of regression, prediction and complex multivariate modeling with many variables.

Christopher Hans joined the statistics faculty in 2005. He serves as co-director of the university’s interdisciplinary undergraduate major in Data Analytics (,, is a Translational Data Analytics (TDA) @ Ohio State Affiliated Faculty member, and is Ohio State’s local coordinator for the annual American Statistical Association (ASA) DataFest @ OSU competition ( He currently serves as an associate editor (AE) of the Journal of Computational and Graphical Statistics, and has served in the past as an AE for the journal Bayesian Analysis and for the journal Computational Statistics and Data Analysis. Professor Hans has also served the profession in various roles in the International Society for Bayesian Analysis and the ASA. His research has appeared in leading statistics journals and has been funded by the National Science Foundation.

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Areas of Expertise
  • Bayesian Statistics
  • Model Uncertainty and Selection
  • Monte Carlo and MCMC Methods
  • Statistical Computing
  • Graphical Models
  • PhD, Duke University (2005)

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(614) 292-7157
Cockins Hall, Room 327
1958 Neil Ave
Columbus, OH 43210