Jeffrey Regier

A champion for advancing diversity in the field of statistics, Jeffrey Regier serves as an Assistant Professor of Statistics at the College of Literature, Science, and the Arts, University of Michigan-Ann Arbor. With a PhD in statistics from UC Berkeley, obtained in 2016, he has established himself as a leading researcher in the areas of graphical models, Bayesian inference, high-performance computing, deep learning, astronomy, and genomics. Professor Regier's work is particularly focused on the development of efficient sequential Monte Carlo methods for approximate Bayesian computation and Markov chain Monte Carlo methods for Bayesian nonparametric models. His innovative approach to these complex statistical challenges has contributed significantly to the advancement of computational methods in statistics. In addition to his research, Jeffrey is deeply committed to teaching and mentoring the next generation of statisticians. He is known for his engaging teaching style and his ability to inspire students to explore the intersections of statistics with other scientific disciplines. His dedication to education is matched by his efforts to foster an inclusive and supportive environment for all students, regardless of their background. Outside of academia, Jeffrey is an avid advocate for the application of statistical methods to real-world problems, particularly in the fields of astronomy and genomics. His interdisciplinary approach not only enriches his research but also broadens the impact of his work beyond traditional statistical boundaries.