Michael Zhu

1.2k total citations
14 papers, 151 citations indexed

About

Michael Zhu is a scholar working on Artificial Intelligence, Statistics and Probability and Information Systems. According to data from OpenAlex, Michael Zhu has authored 14 papers receiving a total of 151 indexed citations (citations by other indexed papers that have themselves been cited), including 8 papers in Artificial Intelligence, 3 papers in Statistics and Probability and 2 papers in Information Systems. Recurrent topics in Michael Zhu's work include Machine Learning and Algorithms (2 papers), Topic Modeling (2 papers) and Gaussian Processes and Bayesian Inference (2 papers). Michael Zhu is often cited by papers focused on Machine Learning and Algorithms (2 papers), Topic Modeling (2 papers) and Gaussian Processes and Bayesian Inference (2 papers). Michael Zhu collaborates with scholars based in United States, Australia and United Kingdom. Michael Zhu's 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 and has published in prestigious journals such as Communications of the ACM, Electronics and Knowledge and Information Systems.

In The Last Decade

Michael Zhu

13 papers receiving 146 citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Michael Zhu United States 5 116 24 18 17 14 14 151
Mikhail Yurochkin United States 6 138 1.2× 13 0.5× 15 0.8× 20 1.2× 8 0.6× 19 169
Shuguo Han Singapore 8 157 1.4× 41 1.7× 15 0.8× 10 0.6× 3 0.2× 9 179
Phillipp Schoppmann United States 5 171 1.5× 30 1.3× 9 0.5× 11 0.6× 2 0.1× 11 189
David I. Inouye United States 6 147 1.3× 70 2.9× 7 0.4× 18 1.1× 8 0.6× 14 204
Christos Louizos Netherlands 5 119 1.0× 12 0.5× 8 0.4× 51 3.0× 5 0.4× 12 145
Róbert Ormándi Hungary 4 115 1.0× 25 1.0× 12 0.7× 16 0.9× 2 0.1× 14 167
Natalia Ponomareva United States 5 139 1.2× 28 1.2× 12 0.7× 5 0.3× 2 0.1× 21 165

Countries citing papers authored by Michael Zhu

Since Specialization
Citations

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

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

14 of 14 papers shown
1.
Ouden, Dirk‐Bart den & Michael Zhu. (2022). Neuromodulation of verb-transitivity judgments. Journal of Neurolinguistics. 63. 101088–101088. 2 indexed citations
2.
Zhu, Michael, et al.. (2022). Using Transformers and Deep Learning with Stance Detection to Forecast Cryptocurrency Price Movement. 2022 13th International Conference on Information and Communication Technology Convergence (ICTC). 1–6. 3 indexed citations
3.
Karami, Amir, et al.. (2022). 2020 U.S. presidential election in swing states: Gender differences in Twitter conversations. International Journal of Information Management Data Insights. 2(2). 100097–100097. 10 indexed citations
4.
Karami, Amir, et al.. (2021). Automatic Categorization of LGBT User Profiles on Twitter with Machine Learning. Electronics. 10(15). 1822–1822. 4 indexed citations
5.
6.
Zhu, Michael, Chang Liu, & Jun Zhu. (2020). Variance Reduction and Quasi-Newton for Particle-Based Variational Inference. International Conference on Machine Learning. 1. 11576–11587. 6 indexed citations
7.
Reese, Timothy G. & Michael Zhu. (2020). LB-CNN: Convolutional Neural Network with Latent Binarization for Large Scale Multi-class Classification. 142–147. 2 indexed citations
8.
Zhu, Michael. (2019). Sample Adaptive MCMC. Neural Information Processing Systems. 32. 9063–9074.
9.
Arora, Sanjeev, Rong Ge, Yoni Halpern, et al.. (2018). Learning topic models -- provably and efficiently. Communications of the ACM. 61(4). 85–93. 8 indexed citations
10.
Zhu, Michael, et al.. (2017). Group Additive Structure Identification for Kernel Nonparametric Regression. Neural Information Processing Systems. 30. 4907–4916. 3 indexed citations
11.
Zhu, Michael & Stefano Ermon. (2015). A Hybrid Approach for Probabilistic Inference using Random Projections. International Conference on Machine Learning. 2039–2047. 6 indexed citations
12.
Zhu, Michael, Anthony Dick, & Anton van den Hengel. (2015). Camera Network Topology Estimation by Lighting Variation. Adelaide Research & Scholarship (AR&S) (University of Adelaide). 2 indexed citations
13.
Zhu, Michael, et al.. (2009). An Innovative Approach Examining the Asymmetrical and Nonlinear Relationship Between Attribute-Level Performance and Service Outcomes. ACR North American Advances. 2 indexed citations
14.
Lin, X. Sheldon, Chris Clifton, & Michael Zhu. (2004). Privacy-preserving clustering with distributed EM mixture modeling. Knowledge and Information Systems. 8(1). 68–81. 101 indexed citations

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.

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