Jeffrey Regier

Jeffrey Regier is an Assistant Professor in the Department of Statistics at the University of Michigan. He is affiliated with the Center for Computational Medicine and Bioinformatics and the Michigan Institute for Data Science. His research focuses on Bayesian methods applied to astronomy and genomics, utilizing tools such as variational inference, sequential Monte Carlo, and diffusion models. He holds a Ph.D. in Statistics and maintains an active research group exploring interdisciplinary applications of statistical theory.

Research interests include advancing statistical methodologies for complex data challenges, particularly in astronomical image analysis, genomic data interpretation, and computational biology. Recent work emphasizes scalable inference techniques and their application to problems like deblending crowded starfields, modeling glioblastoma evolution, and improving accuracy in cell-perturbation experiments. His group collaborates across disciplines to bridge statistical theory with real-world scientific problems.

Publications highlight contributions to variational inference algorithms, neural posterior estimation, and probabilistic modeling in genomics and astrophysics. He has no listed scientific awards but is actively engaged in advancing methodological innovations in Bayesian statistics. His advising role extends to mentoring students in the Ph.D. and Master's programs, focusing on training in computational and theoretical statistics.

Office: 250 West Hall, Department of Statistics, University of Michigan. Contact: regier@umich.edu.