Michael I. Jordan
Michael I. Jordan publishes mostly in Artificial Intelligence, Statistics and Probability and Management Science and Operations Research, on topics such as Bayesian Methods and Mixture Models, Statistical Methods and Inference and Machine Learning and Algorithms.
- World rank
- #4,949 of 7,710,434 ranked researchers
- Rank in United States
- #1,689 of 1,627,098
- Works
- 1,165
- Citations
- 182k
- Citations per work
- 155.9
What does Michael I. Jordan research?
Michael I. Jordan's work is mostly in Computer Science (40%), Mathematics (10%), Decision Sciences (6%).
Their six largest subfields, inside their fields and domains; the grey slice is everything else they publish. The interactive view splits every one of their works down to single topics.
The fields and subfields, with their numbers
- Physical Sciences63054.1%
- Computer Science46740.1%
- Mathematics1159.9%
- Engineering484.1%
- Everything else40134.4%
- Social Sciences706.0%
- Decision Sciences706.0%
- Life Sciences645.5%
- 1Artificial Intelligence36% of their works
- 2Statistics and Probability10% of their works
- 3Management Science and Operations Research6% of their works
- 4Molecular Biology6% of their works
- 5Computational Mechanics4% of their works
- 6Computer Vision and Pattern Recognition4% of their works
Research topics
Which keywords describe Michael I. Jordan's research?
The keywords of their largest research topics: Deep Learning, Convex Optimization, Bayesian Inference, Variational Inference, Neural Networks, Gaussian Processes, Machine Learning and Clustering.
- Probabilistic Models
- Backpropagation Learning
- Bandit Optimization
- Compressed Sensing
- Semi-Supervised Learning
- Active Learning
- Regularization
- Clustering
- Gaussian Processes
- Variational Inference
- Convex Optimization
- Deep Learning
- Bayesian Inference
- Neural Networks
- Machine Learning
- Mixture Models
- Variable Selection
- Random Projections
- Stochastic Gradient Descent
- Sparse Representation
- Bayesian Optimization
- Self-Organizing Maps
- Sparse Regression
Size is their works in the topics tagged with each word, from their 8 largest topics. Each links to the topic it comes from most.
All 23 words, with their numbers
- Deep Learning4214 topics
- Convex Optimization3553 topics
- Bayesian Inference2212 topics
- Variational Inference2212 topics
- Neural Networks2122 topics
- Gaussian Processes2092 topics
- Machine Learning2092 topics
- Clustering1451 topic
- Mixture Models1451 topic
- Regularization1351 topic
- Variable Selection1351 topic
- Active Learning1331 topic
- Random Projections1331 topic
- Semi-Supervised Learning1331 topic
- Stochastic Gradient Descent1331 topic
- Compressed Sensing1231 topic
- Sparse Representation1231 topic
- Bandit Optimization991 topic
- Bayesian Optimization991 topic
- Backpropagation Learning791 topic
- Self-Organizing Maps791 topic
- Probabilistic Models761 topic
- Sparse Regression761 topic
Where does Michael I. Jordan work?
Michael I. Jordan's main affiliation in the publication record is University of California, Berkeley, United States.
How many publications and citations does Michael I. Jordan have?
OpenAlex counts 1,165 works and 181,578 citations for Michael I. Jordan, an h-index of 162, ranking #4,949 of 7,710,434 researchers worldwide on the quality score.
Ranked on field-normalised excellence (50%), output (30%) and citations (20%). Counts come from OpenAlex author records, which occasionally merge different people who share a name or split one person into several.
Papers, co-authors, who cited this work and researchers on the nearest topics have tabs of their own, above. Is this your page? Claim it with your ORCID iD, or request a correction or removal.
Discussion
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