Michael Irwin Jordan

Michael Irwin Jordan is a Professor at the University of California, Berkeley, ranked #1 worldwide in Machine Learning and Distributed Computing, #36 in Computer Science, and #9 in Statistics according to Academic Influence rankings. His interdisciplinary research bridges theoretical foundations with practical applications across machine learning, statistics, and artificial intelligence.

His educational background includes a PhD in Computer Science from the University of California, San Diego, a Master's in Mathematics from Arizona State University, and a Bachelor's in Psychology from Louisiana State University. This diverse foundation has shaped his approach to integrating perspectives from computer science, statistics, neuroscience, and psychology.

Professor Jordan's research spans several decades, beginning with foundational work in neural networks and connectionist models. He made seminal contributions to probabilistic graphical models, variational inference, and Bayesian nonparametrics. His 2001 paper 'Latent Dirichlet Allocation' has received over 34,000 citations, becoming one of the most influential papers in machine learning. More recently, his work has focused on applications to biological data analysis, particularly single-cell transcriptomics, where he has developed probabilistic models and software tools like scvi-tools.

• Member of the National Academy of Engineering (2010)
• Ranked #1 worldwide in Machine Learning
• Ranked #1 worldwide in Distributed Computing
• Ranked #36 worldwide in Computer Science
• Ranked #9 worldwide in Statistics

As an advisor, Professor Jordan has mentored numerous PhD students who have become leaders in academia and industry. His recent work through 2022 shows continued innovation across multiple domains, including uncertainty quantification, theoretical analysis of transfer learning, and scalable frameworks for reinforcement learning. He also contributes significantly to data science education, having helped develop Berkeley's undergraduate data science curriculum.