Zekai J. Gao
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- Graph Theory and Algorithms 2
- Artificial Intelligence top 5%
- Machine Learning and Algorithms 2
- Advanced Text Analysis Techniques 2
- Machine Learning and Data Classification 2
- Signal Processing top 10%
- Data Management and Algorithms 5
- General Social Sciences top 5%
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- Advanced Database Systems and Queries 7
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- Cloud Computing and Resource Management 3
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- Scientific Computing and Data Management 2
- Cited by
- Computer Vision and Pattern RecognitionArtificial IntelligenceStatistical and Nonlinear Physics
- Journals
- IEEE Transactions on Knowledge and Data Engineering (2 papers)Proceedings of the VLDB Endowment (1 paper)ACM Transactions on Knowledge Discovery from Data (1 paper)
- Partner nations
- United StatesChinaHong Kong
In The Last Decade
Zekai J. Gao
13 papers receiving 451 citations
Peers
Comparison fields: 5 of 64
- Computer Vision and Pattern Recognition 234
- Artificial Intelligence 236
- Statistical and Nonlinear Physics 85
- Signal Processing 72
- General Social Sciences 21
Countries citing papers authored by Zekai J. Gao
This map shows the geographic impact of Zekai J. Gao'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 Zekai J. Gao with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Zekai J. Gao more than expected).
Fields of papers citing papers by Zekai J. Gao
This network shows the impact of papers produced by Zekai J. Gao. 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 Zekai J. Gao. The network helps show where Zekai J. Gao may publish in the future.
Co-authorship network
The 23 scholars most cited alongside Zekai J. Gao, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | 2024 | 3 | |
| 2 | 2020 | 10 | |
| 3 | 2019 | 23 | |
| 4 | 2019 | 4 | |
| 5 | 2018 | 8 | |
| 6 | 2018 | 14 | |
| 7 | 2018 | 16 | |
| 8 | 2017 | 32 | |
| 9 | 2017 | 13 | |
| 10 | 2016 | 1 | |
| 11 | 2014 | 34 | |
| 12 | 2011 | 32 | |
| 13 | 2011 | 272 |
About Zekai J. Gao
Zekai J. Gao is a scholar working on Signal Processing, Computer Networks and Communications, Information Systems and Management, Computer Vision and Pattern Recognition and Artificial Intelligence, having authored 13 papers that have together received 462 indexed citations. Recurring topics across this work include Advanced Database Systems and Queries (7 papers), Data Management and Algorithms (5 papers), Cloud Computing and Resource Management (3 papers), Graph Theory and Algorithms (2 papers), Machine Learning and Algorithms (2 papers), Advanced Text Analysis Techniques (2 papers), Machine Learning and Data Classification (2 papers) and Scientific Computing and Data Management (2 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (234 citations), Artificial Intelligence (236 citations), Statistical and Nonlinear Physics (85 citations), Signal Processing (72 citations) and General Social Sciences (21 citations). Zekai J. Gao has collaborated with scholars based in United States, China and Hong Kong. Frequent co-authors include Yangqiu Song, Shi‐Xia Liu, Weiwei Cui, Li Tan, Xin Tong, Conglei Shi, Huamin Qu, Luis L. Perez, Christopher Jermaine and Michael Gubanov. Their work appears in journals such as IEEE Transactions on Knowledge and Data Engineering, Proceedings of the VLDB Endowment, ACM Transactions on Knowledge Discovery from Data, IEEE Transactions on Visualization and Computer Graphics and Applied Soft Computing.
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.