Michael I. Jordan
Impact in
- Artificial Intelligence top 0.01%
- Topic Modeling
- Bayesian Methods and Mixture Models
- Advanced Text Analysis Techniques
- Natural Language Processing Techniques
- Neural Networks and Applications
- Computer Vision and Pattern Recognition top 0.01%
- Face and Expression Recognition
- Advanced Image and Video Retrieval Techniques
Papers in
-
- Bayesian Methods and Mixture Models 91
- Machine Learning and Algorithms 69
- Stochastic Gradient Optimization Techniques 51
- Neural Networks and Applications 50
- Bayesian Modeling and Causal Inference 46
- Gaussian Processes and Bayesian Inference 43
-
- Statistical Methods and Inference 62
- Co-authors
- Andrew Y. Ng (12 shared papers)David M. Blei (16 shared papers)Tom M. Mitchell (1 shared paper)Martin J. Wainwright (50 shared papers)Zoubin Ghahramani (9 shared papers)Robert A. Jacobs (13 shared papers)Yair Weiss (4 shared papers)Emanuel Todorov (3 shared papers)
- Journals
- Journal of Machine Learning Research (13 papers)Proceedings of the National Academy of Sciences (9 papers)Neural Computation (8 papers)Bioinformatics (7 papers)Journal of the American Statistical Association (7 papers)
- Partner nations
- United StatesChinaUnited Kingdom
In The Last Decade
Michael I. Jordan
527 papers receiving 90.4k citations
Michael I. Jordan's Hit Papers
Peers
Comparison fields: 5 of 241
- Artificial Intelligence 48.1k
- Computer Vision and Pattern Recognition 19.3k
- Signal Processing 8.5k
- Computational Mathematics 434
- Software 2.3k
Countries citing papers authored by Michael I. Jordan
This map shows the geographic impact of Michael I. Jordan's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Michael I. Jordan with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Michael I. Jordan more than expected).
Fields of papers citing papers by Michael I. Jordan
This network shows the impact of papers produced by Michael I. Jordan. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Michael I. Jordan. The network helps show where Michael I. Jordan may publish in the future.
Co-authors
The 25 scholars most cited alongside Michael I. Jordan, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
Showing the 20 most-cited of 556 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | Latent dirichlet allocation Hit paper breakdown → | 2003 | 18102 |
| 2 | Machine learning: Trends, perspectives, and prospects Hit paper breakdown → | 2015 | 6036 |
| 3 | On Spectral Clustering: Analysis and an algorithm Hit paper breakdown → | 2001 | 5099 |
| 4 | Adaptive Mixtures of Local Experts Hit paper breakdown → | 1991 | 2833 |
| 5 | An Internal Model for Sensorimotor Integration Hit paper breakdown → | 1995 | 2430 |
| 6 | Hierarchical Dirichlet Processes Hit paper breakdown → | 2006 | 2248 |
| 7 | Optimal feedback control as a theory of motor coordination Hit paper breakdown → | 2002 | 2121 |
| 8 | An Introduction to Variational Methods for Graphical Models Hit paper breakdown → | 1999 | 2039 |
| 9 | Distance Metric Learning with Application to Clustering with Side-Information Hit paper breakdown → | 2002 | 1730 |
| 10 | Hierarchical Mixtures of Experts and the EM Algorithm Hit paper breakdown → | 1994 | 1646 |
| 11 | Learning in Graphical Models Hit paper breakdown → | 1998 | 1581 |
| 12 | Trust Region Policy Optimization Hit paper breakdown → | 2015 | 1573 |
| 13 | Graphical Models, Exponential Families, and Variational Inference Hit paper breakdown → | 2007 | 1562 |
| 14 | An Introduction to MCMC for Machine Learning Hit paper breakdown → | 2003 | 1483 |
| 15 | On Discriminative vs. Generative Classifiers: A comparison of logistic regression and naive Bayes Hit paper breakdown → | 2001 | 1261 |
| 16 | Graphical Models, Exponential Families, and Variational Inference Hit paper breakdown → | 2008 | 1097 |
| 17 | Deep generative modeling for single-cell transcriptomics Hit paper breakdown → | 2018 | 1089 |
| 18 | Forward Models: Supervised Learning with a Distal Teacher Hit paper breakdown → | 1992 | 1014 |
| 19 | Multiple kernel learning, conic duality, and the SMO algorithm Hit paper breakdown → | 2004 | 943 |
| 20 | Convex and Semi-Nonnegative Matrix Factorizations Hit paper breakdown → | 2008 | 920 |
About Michael I. Jordan
Michael I. Jordan is a scholar working on Artificial Intelligence, Statistics and Probability, Molecular Biology, Computer Vision and Pattern Recognition and Computer Networks and Communications, having authored 556 papers that have together received 96.0k indexed citations. Recurring topics across this work include Bayesian Methods and Mixture Models (91 papers), Machine Learning and Algorithms (69 papers), Statistical Methods and Inference (62 papers), Sparse and Compressive Sensing Techniques (53 papers), Stochastic Gradient Optimization Techniques (51 papers), Neural Networks and Applications (50 papers), Bayesian Modeling and Causal Inference (46 papers) and Gaussian Processes and Bayesian Inference (43 papers). The work is most often cited by research in Artificial Intelligence (48.1k citations), Computer Vision and Pattern Recognition (19.3k citations), Signal Processing (8.5k citations), Computational Mathematics (434 citations) and Software (2.3k citations). Michael I. Jordan has collaborated with scholars based in United States, China and United Kingdom. Frequent co-authors include Andrew Y. Ng, David M. Blei, Tom M. Mitchell, Martin J. Wainwright, Zoubin Ghahramani, Robert A. Jacobs, Yair Weiss, Emanuel Todorov, Tommi Jaakkola and Francis Bach. Their work appears in journals such as Journal of Machine Learning Research, Proceedings of the National Academy of Sciences, Neural Computation, Bioinformatics and Journal of the American Statistical Association.
Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.