Chao Wu
- Cognitive Neuroscience top 2%
- Signal Processing top 2%
- Human-Computer Interaction top 2%
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- Multimodal Machine Learning Applications 5
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- Privacy-Preserving Technologies in Data 15
- Domain Adaptation and Few-Shot Learning 8
- Anomaly Detection Techniques and Applications 8
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- Web Data Mining and Analysis 8
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- Peer-to-Peer Network Technologies 5
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- Traffic Prediction and Management Techniques 5
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- Complex Network Analysis Techniques 5
- Journals
- SHILAP Revista de lepidopterología (1 paper)PLoS ONE (1 paper)Journal of Cleaner Production (1 paper)
- Partner nations
- ChinaUnited KingdomUnited States
In The Last Decade
Chao Wu
90 papers receiving 2.3k citations
Hit Papers
Peers
Comparison fields: 5 of 145
- Cognitive Neuroscience 980
- Experimental and Cognitive Psychology 368
- Signal Processing 285
- Human-Computer Interaction 142
- Computer Vision and Pattern Recognition 359
Countries citing papers authored by Chao Wu
This map shows the geographic impact of Chao Wu'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 Chao Wu with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Chao Wu more than expected).
Fields of papers citing papers by Chao Wu
This network shows the impact of papers produced by Chao Wu. 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 Chao Wu. The network helps show where Chao Wu may publish in the future.
Co-authorship network
The 25 scholars most cited alongside Chao Wu, 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 | 2025 | 2 | |
| 2 | 2024 | 1 | |
| 3 | 2024 | 1 | |
| 4 | 2023 | 43 | |
| 5 | 2023 | 2 | |
| 6 | 2023 | 23 | |
| 7 | 2023 | 0 | |
| 8 | 2023 | 0 | |
| 9 | 2023 | 27 | |
| 10 | 2022 | 6 | |
| 11 | 2022 | 22 | |
| 12 | 2021 | 18 | |
| 13 | 2021 | 6 | |
| 14 | 2020 | 14 | |
| 15 | 2020 | 101 | |
| 16 | 2019 | 7 | |
| 17 | 2019 | 1 | |
| 18 | 2018 | 59 | |
| 19 | 2017 | 153 | |
| 20 | TAGS ARE RELATED: MEASUREMENT OF SEMANTIC RELATEDNESS BASED ON FOLKSONOMY NETWORK | 2011 | 2 |
About Chao Wu
Chao Wu is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition and Information Systems, having authored 97 papers that have together received 2.4k indexed citations. Recurring topics across this work include Privacy-Preserving Technologies in Data (15 papers), Domain Adaptation and Few-Shot Learning (8 papers), Web Data Mining and Analysis (8 papers), Anomaly Detection Techniques and Applications (8 papers), Peer-to-Peer Network Technologies (5 papers), Traffic Prediction and Management Techniques (5 papers), Multimodal Machine Learning Applications (5 papers) and Complex Network Analysis Techniques (5 papers). The work is most often cited by research in Cognitive Neuroscience (980 citations), Experimental and Cognitive Psychology (368 citations) and Signal Processing (285 citations). Chao Wu has collaborated with scholars based in China, United Kingdom and United States. Frequent co-authors include Yike Guo, Hao Dong, Akara Supratak, Simiao Yu, Paul M. Matthews, Wei Pan, Simon Hu, Jun Xiao, Xiaoxiang Na and Julien Lépine. Their work appears in journals such as SHILAP Revista de lepidopterología, PLoS ONE and Journal of Cleaner Production.
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.