Michael I. Jordan
Professor Emeritus and Professor in the Graduate School at UC Berkeley
A statistical machine-learning researcher whose work on probabilistic models and inference helped shape the mathematical foundations of modern AI.
Biography
Michael I. Jordan is an American researcher associated with the University of California, Berkeley, working across machine learning, statistics, and related sciences. He earned a doctorate in cognitive science at the University of California, San Diego, and taught at MIT before moving to Berkeley. His research connects computational methods with the mathematical treatment of uncertainty.
Jordan co-authored Latent Dirichlet Allocation with David Blei and Andrew Ng in 2003. The method represents documents as mixtures of latent topics and became an influential approach to probabilistic modeling of text collections. His wider work includes graphical models, variational inference, optimization, and the foundations of learning from data.
He has also contributed through teaching and mentoring researchers who developed major areas of machine learning. His perspective extends beyond prediction accuracy to the behavior of systems that combine data, decisions, and human institutions. Jordan’s place in AI rests on sustained foundational research and education, connecting practical algorithms with questions about what their mathematical assumptions permit them to learn.
Notable contributions
Co-authored Latent Dirichlet Allocation
Developed a generative probabilistic model for discrete collections such as text corpora with David Blei and Andrew Ng.
Received the IEEE John von Neumann Medal
Recognized for contributions to machine learning and data science.
Career history
Professor