Jon McAuliffe

Jon McAuliffe is an Assistant Professor at the Department of Statistics, University of California, Berkeley. He earned his Ph.D. in Statistics at Berkeley in 2005 under Michael Jordan, with a dissertation titled "Statistical Methods for Comparing Genomes." His research spans statistical inference, machine learning, and their applications in astronomy, computational biology, and theoretical statistics.

• Department: Statistics
• University: University of California, Berkeley
• Academic Rank: Assistant Professor
• Ph.D. Year: 2005
• Advisor: Michael Jordan

McAuliffe's research interests include Bayesian inference, variational methods, extreme event modeling, and scalable statistical algorithms. His work addresses challenges in astronomical image analysis, cancer genomics, and probabilistic modeling of complex datasets.

Recent publications focus on time-uniform confidence sequences, deblending starfields, and applications in climate science. While no specific awards are listed, his contributions to methodological statistics and interdisciplinary applications are notable.

He has not been explicitly linked to advising students in the provided texts but has collaborated on projects like the Celeste astronomical catalog, emphasizing computational efficiency and probabilistic modeling at petascale levels.