Caleb Chen Cao

1.0k citations
26 papers · 647 · h-index 11

Impact in

Papers in

    • Explainable Artificial Intelligence (XAI) 9
    • Topic Modeling 5
    • Adversarial Robustness in Machine Learning 3
    • Machine Learning in Healthcare 3
    • Advanced Graph Neural Networks 3
    • Mobile Crowdsensing and Crowdsourcing 8

Caleb Chen Cao

25 papers receiving 635 citations

Peers

Caleb Chen Cao
Comparison fields: 5 of 76
  • Computer Science Applications 282
  • Health Informatics 30
  • Transportation 80
  • Artificial Intelligence 355
  • Management Science and Operations Research 129
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Qika Lin China
Laurent Charlin Canada
Naama Zwerdling Israel
Vincent Bindschaedler United States
Dong‐Kyu Chae South Korea
Bingqing Qu Switzerland
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Nathalie Baracaldo United States
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Citations per field
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Citations per year

Countries citing papers authored by Caleb Chen Cao

Since Specialization
Citations

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

Fields of papers citing papers by Caleb Chen Cao

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Caleb Chen Cao. 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 Caleb Chen Cao. The network helps show where Caleb Chen Cao may publish in the future.

Co-authors

The 25 scholars most cited alongside Caleb Chen Cao, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Caleb Chen Cao Line = papers co-authored together Caleb Chen Cao links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 26 papers — load more, or switch the sort, to bring in the rest.

#Work
1 2020159
2 2012107
3 201495
4 201650
5 201540
6 201439
7 201429
8 202123
9 201320
10 202213
11 202311
12 201410
13 20237
14 20227
15 20146
16 20156
17 20226
18 20216
19 20253
20 20233

About Caleb Chen Cao

Caleb Chen Cao is a scholar working on Artificial Intelligence, Computer Science Applications, Computer Vision and Pattern Recognition, Information Systems and Management Science and Operations Research, having authored 26 papers that have together received 647 indexed citations. Recurring topics across this work include Explainable Artificial Intelligence (XAI) (9 papers), Mobile Crowdsensing and Crowdsourcing (8 papers), Topic Modeling (5 papers), Adversarial Robustness in Machine Learning (3 papers), Expert finding and Q&A systems (3 papers), Machine Learning in Healthcare (3 papers), Auction Theory and Applications (3 papers) and Advanced Graph Neural Networks (3 papers). The work is most often cited by research in Computer Science Applications (282 citations), Health Informatics (30 citations), Transportation (80 citations), Artificial Intelligence (355 citations) and Management Science and Operations Research (129 citations). Caleb Chen Cao has collaborated with scholars based in Hong Kong, China and United States. Frequent co-authors include Lei Chen, Yongxin Tong, Jieying She, Xiaohui Li, Yuhan Shi, Wei Bai, Chen Zhang, Han Gao, Cong Wang and Shenjia Zhang. Their work appears in journals such as IEEE Transactions on Visualization and Computer Graphics, IEEE Transactions on Knowledge and Data Engineering, Proceedings of the VLDB Endowment, British Journal of Psychology and ACM Transactions on 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.

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