Kean Ming Tan

992 total citations
25 papers, 440 citations indexed

About

Kean Ming Tan is a scholar working on Statistics and Probability, Artificial Intelligence and Computational Mechanics. According to data from OpenAlex, Kean Ming Tan has authored 25 papers receiving a total of 440 indexed citations (citations by other indexed papers that have themselves been cited), including 16 papers in Statistics and Probability, 8 papers in Artificial Intelligence and 6 papers in Computational Mechanics. Recurrent topics in Kean Ming Tan's work include Statistical Methods and Inference (16 papers), Sparse and Compressive Sensing Techniques (6 papers) and Statistical Methods and Bayesian Inference (4 papers). Kean Ming Tan is often cited by papers focused on Statistical Methods and Inference (16 papers), Sparse and Compressive Sensing Techniques (6 papers) and Statistical Methods and Bayesian Inference (4 papers). Kean Ming Tan collaborates with scholars based in United States, Canada and China. Kean Ming Tan's co-authors include Daniela Witten, Wen‐Xin Zhou, Xuming He, Maitreya J. Dunham, Anna B. Sunshine, Célia Payen, Ivan Liachko, Giang T. Ong, Tong Zhang and Ali Shojaie and has published in prestigious journals such as Proceedings of the National Academy of Sciences, Journal of the American Statistical Association and PLoS Biology.

In The Last Decade

Kean Ming Tan

22 papers receiving 432 citations

Peers

Kean Ming Tan
Comparison fields: 5 of 91
  • Statistics and Probability 145
  • Molecular Biology 93
  • Artificial Intelligence 82
  • Cognitive Neuroscience 60
  • Computational Mechanics 57
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Niels Richard Hansen Denmark View profile →
Citations per field, relative to Kean Ming Tan
Kean Ming Tan · 1×
Citations per year, relative to Kean Ming Tan
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Countries citing papers authored by Kean Ming Tan

Since Specialization
Citations

This map shows the geographic impact of Kean Ming Tan'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 Kean Ming Tan with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Kean Ming Tan more than expected).

Fields of papers citing papers by Kean Ming Tan

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Kean Ming Tan. 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 Kean Ming Tan. The network helps show where Kean Ming Tan may publish in the future.

Co-authorship network of co-authors of Kean Ming Tan

This figure shows the co-authorship network connecting the top 25 collaborators of Kean Ming Tan. A scholar is included among the top collaborators of Kean Ming Tan 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 Kean Ming Tan. Kean Ming Tan is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
# Work Indexed citations
1 0
2 0
3 0
4 4
5 2
6 9
7 7
8 9
9 51
10 15
11 20
12 45
13
Distributionally Robust Reduced Rank Regression and Principal Component Analysis in High Dimensions.
2
14
Graphical nonconvex optimization via an adaptive convex relaxation
3
15 5
16 69
17 32
18 32
19 6
20 49

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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