Zhuochen Jin
Impact in
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- Data Visualization and Analytics
- Video Analysis and Summarization
- Health Informatics top 10%
Papers in
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- Data Visualization and Analytics 9
- Video Analysis and Summarization 2
-
- Anomaly Detection Techniques and Applications 3
- Advanced Text Analysis Techniques 2
- Co-authors
- Nan Cao (13 shared papers)Shunan Guo (7 shared papers)David Gotz (5 shared papers)Jiajun Deng (2 shared papers)Yijiu Ren (2 shared papers)Haipeng Liu (1 shared paper)Yunlang She (1 shared paper)Hang Su (1 shared paper)
- Journals
- IEEE Transactions on Visualization and Computer Graphics (5 papers)Journal of the American Medical Informatics Association (1 paper)Data Mining and Knowledge Discovery (1 paper)JAMA Network Open (1 paper)Journal of Visualization (3 papers)
- Partner nations
- ChinaUnited StatesGermany
In The Last Decade
Zhuochen Jin
14 papers receiving 506 citations
Peers
Comparison fields: 5 of 92
- Computer Vision and Pattern Recognition 184
- Health Informatics 11
- Computational Mathematics 5
- Signal Processing 54
- Artificial Intelligence 148
Countries citing papers authored by Zhuochen Jin
This map shows the geographic impact of Zhuochen Jin'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 Zhuochen Jin with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Zhuochen Jin more than expected).
Fields of papers citing papers by Zhuochen Jin
This network shows the impact of papers produced by Zhuochen Jin. 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 Zhuochen Jin. The network helps show where Zhuochen Jin may publish in the future.
Co-authors
The 25 scholars most cited alongside Zhuochen Jin, 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 | 2020 | 198 | |
| 2 | 2021 | 81 | |
| 3 | 2021 | 50 | |
| 4 | 2018 | 49 | |
| 5 | 2020 | 33 | |
| 6 | 2022 | 21 | |
| 7 | 2018 | 21 | |
| 8 | 2019 | 14 | |
| 9 | 2020 | 9 | |
| 10 | 2021 | 9 | |
| 11 | 2021 | 9 | |
| 12 | 2023 | 7 | |
| 13 | 2019 | 6 | |
| 14 | 2021 | 2 | |
| 15 | 2026 | 0 |
About Zhuochen Jin
Zhuochen Jin is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence, Signal Processing, Sociology and Political Science and Statistical and Nonlinear Physics, having authored 15 papers that have together received 509 indexed citations. Recurring topics across this work include Data Visualization and Analytics (9 papers), Anomaly Detection Techniques and Applications (3 papers), Time Series Analysis and Forecasting (3 papers), Advanced Text Analysis Techniques (2 papers), Multimedia Communication and Technology (2 papers), Video Analysis and Summarization (2 papers), Complex Network Analysis Techniques (2 papers) and Human Mobility and Location-Based Analysis (2 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (184 citations), Health Informatics (11 citations), Computational Mathematics (5 citations), Signal Processing (54 citations) and Artificial Intelligence (148 citations). Zhuochen Jin has collaborated with scholars based in China, United States and Germany. Frequent co-authors include Nan Cao, Shunan Guo, David Gotz, Jiajun Deng, Yijiu Ren, Haipeng Liu, Yunlang She, Hang Su, Dong Xie and Lei Zhang. Their work appears in journals such as IEEE Transactions on Visualization and Computer Graphics, Journal of the American Medical Informatics Association, Data Mining and Knowledge Discovery, JAMA Network Open and Journal of Visualization.
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.