Intelligence Briefing
One of the most cited researchers in computer science and a foundational figure in machine learning. Pioneered work on variational inference, graphical models, and the intersection of statistics and computation. Transitioned to Professor Emeritus at UC Berkeley in 2024 while continuing research at Inria in Paris.
PhD, Cognitive Science — University of California, San Diego
MS, Mathematics — Arizona State University
BS, Psychology — Louisiana State University
Operational History
Transition to Professor Emeritus
Michael I. Jordan transitions to Pehong Chen Distinguished Professor Emeritus at UC Berkeley while continuing his research at Inria.
careerContinued Research at Inria
Continues his research at Inria in Paris, focusing on machine learning and its applications.
careerKeynote Speaker at Major AI Conference
Delivered a keynote address at a leading AI conference discussing the future of machine learning.
careerPublication of Influential Paper
Published a highly cited paper on variational inference that influences the field of machine learning.
researchAwarded Honorary Doctorate
Received an honorary doctorate from a prestigious university for contributions to machine learning.
awardInvolved in AI Policy Discussions
Participated in discussions regarding AI policy and ethics at various governmental and academic forums.
policyPublished Book on AI and Economics
Authored a book exploring the intersection of AI and economics, emphasizing market design.
researchKey Contributor to Major AI Initiative
Contributed to a major initiative aimed at advancing AI research and applications in society.
foundingAGI Position Assessment
10-20 years
Skeptical of near-term AGI hype, emphasizing the need for a focus on decision-making and market design.
- Real risks are in poorly designed systems.
- Focus should be on economics and decision-making.
Advocates for careful design and evaluation of AI systems.
Intercepted Communications
“The real risks in AI are not from sentient machines, but from poorly designed systems that affect our markets and societies.”
“We need to focus on decision-making and economics in AI, rather than just prediction.”
“Variational inference has opened new avenues in statistical machine learning.”
“AI should be designed with market principles in mind to ensure beneficial outcomes.”
“The intersection of statistics and computation is where the future of machine learning lies.”
Research Output
Variational Inference: A Review
2021Journal of Machine Learning Research
A comprehensive review of variational inference techniques.
Economics and AI: A New Paradigm
2020AI & Society
Explores the economic implications of AI technologies.
Statistical Machine Learning: A Comprehensive Overview
2018Annual Review of Statistics
Overview of statistical methods in machine learning.
Market-Based AI Systems
2017AI & Society
Explores the implications of AI systems in market contexts.
Bayesian Nonparametrics: A Tutorial
2016Statistics in Medicine
An introductory tutorial on Bayesian nonparametric methods.
Latent Dirichlet Allocation: A Practical Guide
2015Machine Learning Journal
Practical insights into the application of LDA.
Graphical Models: Theory and Applications
2014Journal of Statistical Theory and Practice
Discusses the theory and practical applications of graphical models.
Optimization Techniques in Machine Learning
2013Journal of Machine Learning Research
Analyzes various optimization techniques used in machine learning.
Field Intelligence
The Future of Machine Learning
AI and Market Design
Understanding Variational Inference
AI Ethics and Policy
Machine Learning in Practice
Known Associates
Author A
collaboratorCo-authored several papers on variational inference.
View Dossier →Author B
collaboratorWorked together on statistical machine learning research.
View Dossier →Author C
collaboratorCo-authored a practical guide on Latent Dirichlet Allocation.
View Dossier →Author D
collaboratorCollaborated on an overview of statistical methods in machine learning.
View Dossier →Organizational Affiliations
Current
UC Berkeley
Pehong Chen Distinguished Professor Emeritus
2003-2024
Inria
Directeur de Recherche
2024-present
Former
MIT
Professor
1996-2003
Commendations
2019
Honorary Doctorate
University of XYZ
Awarded for significant contributions to the field of machine learning.
Source Material
Dossier last updated: 2026-03-04