Michael I. Jordan is one of the most influential figures in machine learning, long based at the University of California, Berkeley, where his titles include Pehong Chen Distinguished Professor Emeritus across the Department of EECS and the Department of Statistics. He has also held a research position at Inria and the Ecole Normale Superieure in Paris.
Jordan is widely credited with helping merge machine learning with statistics, bringing rigorous probabilistic thinking to a field that had often leaned on heuristics. His work spans graphical models, variational inference methods for approximate Bayesian computation, and Latent Dirichlet Allocation, the topic model he co-developed that became a standard tool for discovering themes in large text collections. He has mentored an exceptional lineage of students who themselves became leaders in the field, including Andrew Ng.
He is a member of the National Academy of Sciences and the National Academy of Engineering and a Fellow of the IEEE, ACM, and American Statistical Association. His insistence on statistical foundations continues to shape how the field reasons about uncertainty, inference, and the limits of learning from data.