P. Richard Hahn’s current research concerns the design of latent variable models for scientific and economic data. Ideally, such models are able to match the predictive power of inscrutable “black-box” approaches while also permitting detailed inferential analysis necessary to further theoretical understanding.
He most recently has experience as a statistical consultant for Deloitte Consulting, where he worked with a team in the exploratory analysis of proprietary financial database records, chiefly multivariate dynamic time series. Additionally, he worked as a research intern at Yahoo! Research, where he investigated the use of covariate dependent Dirichlet process mixtures of regressions for recommender system engines.
Hahn earned his PhD in statistical science from Duke University. Additionally, his academic endeavors have led to an MS in mathematics with operations research and statistics option from the New Mexico Institute of Mining and Technology, and a BA in economics-philosophy from Columbia University.
Hahn joined the University of Chicago faculty in 2011.