Michael Zhu
- Artificial Intelligence top 10%
- Information Systems
- Sociology and Political Science
- Computer Vision and Pattern Recognition
- Statistics and Probability
- Co-authors
- Chris CliftonX. Sheldon LinStefano ErmonAmir KaramiChang LiuJun ZhuDavid MimnoDavid Sontag
- Topics
- Machine Learning and Algorithms (2 papers)Topic Modeling (2 papers)Gaussian Processes and Bayesian Inference (2 papers)
- Partner nations
- United StatesAustraliaUnited Kingdom
In The Last Decade
Michael Zhu
13 papers receiving 146 citations
Peers
Comparison fields: 5 of 53
- Artificial Intelligence 116
- Information Systems 24
- Sociology and Political Science 18
- Computer Vision and Pattern Recognition 17
- Statistics and Probability 14
Countries citing papers authored by Michael Zhu
This map shows the geographic impact of Michael Zhu'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 Zhu with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Michael Zhu more than expected).
Fields of papers citing papers by Michael Zhu
This network shows the impact of papers produced by Michael Zhu. 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 Zhu. The network helps show where Michael Zhu may publish in the future.
Co-authorship network of co-authors of Michael Zhu
This figure shows the co-authorship network connecting the top 25 collaborators of Michael Zhu. A scholar is included among the top collaborators of Michael Zhu based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Michael Zhu. Michael Zhu is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 2 | |
| 2 | 3 | |
| 3 | 10 | |
| 4 | 4 | |
| 5 | 2 | |
| 6 | Variance Reduction and Quasi-Newton for Particle-Based Variational Inference | 6 |
| 7 | 2 | |
| 8 | Sample Adaptive MCMC | 0 |
| 9 | 8 | |
| 10 | Group Additive Structure Identification for Kernel Nonparametric Regression | 3 |
| 11 | A Hybrid Approach for Probabilistic Inference using Random Projections | 6 |
| 12 | 2 | |
| 13 | An Innovative Approach Examining the Asymmetrical and Nonlinear Relationship Between Attribute-Level Performance and Service Outcomes | 2 |
| 14 | 101 |
About Michael Zhu
Michael Zhu is a scholar working on Statistics and Probability, General Social Sciences and Artificial Intelligence, having authored 14 papers that have together received 151 indexed citations. Recurring topics across this work include Machine Learning and Algorithms (2 papers), Topic Modeling (2 papers) and Gaussian Processes and Bayesian Inference (2 papers). The work is most often cited by research in Artificial Intelligence (116 citations), Computational Mathematics (2 citations) and Computer Science Applications (10 citations). Michael Zhu has collaborated with scholars based in United States, Australia and United Kingdom. Frequent co-authors include Chris Clifton, X. Sheldon Lin, Stefano Ermon, Amir Karami, Chang Liu, Jun Zhu, David Mimno, David Sontag, Sanjeev Arora and Yoni Halpern. Their work appears in journals such as Communications of the ACM, Electronics and Knowledge and Information Systems.
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.