2026 Michael I. Jordan: Computer Science Researcher – H-Index, Publications & Awards

Discipline name	D-Index	World Ranking	Current World Ranking	National Ranking	Current National Ranking	Publications	Citations
Best Scientists	200	287	271	189	177	917	271056
Computer Science	198	5	5	2	2	842	265704

Research.com Recognitions

2026 - Research.com Computer Science in United States Leader Award
2025 - Research.com Best Scientists Award
2025 - Research.com Computer Science in United States Leader Award
2023 - Research.com Computer Science in United States Leader Award
2022 - Research.com Computer Science in United States Leader Award
2020 - IEEE John von Neumann Medal “For contributions to machine learning and data science.”
2015 - David E. Rumelhart Prize for Contributions to the Theoretical Foundations of Human Cognition
2012 - SIAM Fellow For contributions to machine learning, in particular variational approaches to statistical inference.
2011 - Fellow of the American Academy of Arts and Sciences
2010 - Member of the National Academy of Engineering For contributions to the foundations and applications of machine learning.
2010 - ACM Fellow For contributions to the theory and application of machine learning.
2010 - Member of the National Academy of Sciences
2009 - ACM AAAI Allen Newell Award For fundamental advances in machine learning, particularly his groundbreaking work on graphical models and nonparametric Bayesian statistics, the broad application of this work across computer science, statistics, and the biological sciences.
2007 - Fellow of the American Statistical Association (ASA)
2006 - Fellow of the American Association for the Advancement of Science (AAAS)
2005 - IEEE Fellow For contributions to probabilistic graphical models and neural information processing systems.
2002 - Fellow of the Association for the Advancement of Artificial Intelligence (AAAI) For significant contributions to reasoning under uncertainty, machine learning, and human motor control.

Overview

Michael I. Jordan is affiliated with the University of California, Berkeley, in the United States. Their research spans multiple fields with a primary focus on computer science. Subfields explored include artificial intelligence, management science and operations research, statistics and probability, computational mechanics, and molecular biology.

The scientist has contributed extensively to the following topics:

Advanced Bandit Algorithms Research
Sparse and Compressive Sensing Techniques
Stochastic Gradient Optimization Techniques
Statistical Methods and Inference
Auction Theory and Applications
Machine Learning and Algorithms
Reinforcement Learning in Robotics

Frequent co-authors who have collaborated on numerous occasions include:

Tianyi Lin
Anastasios N. Angelopoulos
Nir Yosef
Zhaoran Wang
Jiantao Jiao

Michael I. Jordan publishes predominately in venues such as:

arXiv (Cornell University)
bioRxiv (Cold Spring Harbor Laboratory)
Proceedings of the AAAI Conference on Artificial Intelligence
SSRN Electronic Journal
Harvard Data Science Review

Recent publications include:

"A Python library for probabilistic analysis of single-cell omics data," 2022, Nature Biotechnology
"Probabilistic harmonization and annotation of single-cell transcriptomics data with deep generative models," 2021, Molecular Systems Biology
"Skilful nowcasting of extreme precipitation with NowcastNet," 2023, Nature
"MultiVI: deep generative model for the integration of multimodal data," 2023, Nature Methods
"DestVI identifies continuums of cell types in spatial transcriptomics data," 2022, Nature Biotechnology

The scientist has been recognized with multiple awards and honors throughout their career:

IEEE John von Neumann Medal (2020) "For contributions to machine learning and data science."
David E. Rumelhart Prize for Contributions to the Theoretical Foundations of Human Cognition (2015)
SIAM Fellow (2012) For contributions to machine learning, particularly variational approaches to statistical inference.
Fellow of the American Academy of Arts and Sciences (2011)
Member of the National Academy of Sciences (2010)
Member of the National Academy of Engineering (2010) For contributions to the foundations and applications of machine learning.
ACM Fellow (2010) For contributions to the theory and application of machine learning.
ACM AAAI Allen Newell Award (2009) For fundamental advances in machine learning, particularly graphical models and nonparametric Bayesian statistics, and the broad application across computer science, statistics, and biological sciences.
Fellow of the American Statistical Association (2007)
Fellow of the American Association for the Advancement of Science (2006)
IEEE Fellow (2005) For contributions to probabilistic graphical models and neural information processing systems.
Fellow of the Association for the Advancement of Artificial Intelligence (2002) For significant contributions to reasoning under uncertainty, machine learning, and human motor control.

Best Publications

Latent dirichlet allocation

David M. Blei;Andrew Y. Ng;Michael I. Jordan

44013
Citations
On Spectral Clustering: Analysis and an algorithm

Andrew Y. Ng;Michael I. Jordan;Yair Weiss

11187
Citations
Machine learning: Trends, perspectives, and prospects

M. I. Jordan;T. M. Mitchell

9722
Citations
Adaptive mixtures of local experts

Robert A. Jacobs;Michael I. Jordan;Steven J. Nowlan;Geoffrey E. Hinton

6071
Citations
Trust Region Policy Optimization

John Schulman;Sergey Levine;Pieter Abbeel;Michael Jordan

5094
Citations
Graphical Models, Exponential Families, and Variational Inference

Martin J. Wainwright;Michael I. Jordan

4798
Citations
Sharing Clusters among Related Groups: Hierarchical Dirichlet Processes

Yee W. Teh;Michael I. Jordan;Matthew J. Beal;David M. Blei

4473
Citations
Hierarchical mixtures of experts and the EM algorithm

Michael I. Jordan;Robert A. Jacobs

4319
Citations
An Internal Model for Sensorimotor Integration

Daniel M. Wolpert;Zoubin Ghahramani;Michael I. Jordan

4316
Citations
Distance Metric Learning with Application to Clustering with Side-Information

Eric P. Xing;Michael I. Jordan;Stuart J Russell;Andrew Y. Ng

3829
Citations
An introduction to variational methods for graphical models

Michael I. Jordan;Zoubin Ghahramani;Tommi S. Jaakkola;Lawrence K. Saul

3761
Citations
Optimal feedback control as a theory of motor coordination.

Emanuel Todorov;Michael I. Jordan

3735
Citations
Learning Transferable Features with Deep Adaptation Networks

Mingsheng Long;Mingsheng Long;Yue Cao;Jianmin Wang;Michael Jordan

3589
Citations
An introduction to MCMC for machine learning

Christophe Andrieu;Nando De Freitas;Arnaud Doucet;Michael I. Jordan

3437
Citations
On Discriminative vs. Generative Classifiers: A comparison of logistic regression and naive Bayes

Andrew Y. Ng;Michael I. Jordan

3270
Citations
Kalman filtering with intermittent observations

B. Sinopoli;L. Schenato;M. Franceschetti;K. Poolla

3145
Citations
Learning the Kernel Matrix with Semidefinite Programming

Gert R. G. Lanckriet;Nello Cristianini;Peter Bartlett;Laurent El Ghaoui

3089
Citations
Learning in graphical models

Michael I. Jordan

2836
Citations
Trust Region Policy Optimization

John Schulman;Sergey Levine;Philipp Moritz;Michael I. Jordan

2810
Citations
Active learning with statistical models

David A. Cohn;Zoubin Ghahramani;Michael I. Jordan

2701
Citations
Attractor dynamics and parallelism in a connectionist sequential machine

Michael I. Jordan

2647
Citations

Frequent Co-Authors

Martin J. Wainwright MIT

Peter L. Bartlett University of California, Berkeley

Francis Bach École Normale Supérieure

Benjamin Recht University of California, Berkeley

John C. Duchi Stanford University

Mingsheng Long Tsinghua University

David M. Blei Columbia University

Ameet Talwalkar Carnegie Mellon University

David A. Patterson University of California, Berkeley

Zoubin Ghahramani University of Cambridge

External Links

Wikipedia of Michael I. Jordan Personal website of Michael I. Jordan Google Scholar page

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