Siyuan Huang
- Developmental Neuroscience top 5%
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- Multimodal Machine Learning Applications 14
- Human Pose and Action Recognition 11
- Neurology top 5%
- Geology top 5%
- 3D Surveying and Cultural Heritage 5
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- Medical Imaging Techniques and Applications 7
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- Natural Language Processing Techniques 6
- Speech and dialogue systems 4
- Topic Modeling 4
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- Robot Manipulation and Learning 5
- Co-authors
- Song‐Chun ZhuYixin ZhuSiyuan QiJohn C. MazziottaStuart A. SchneckScott T. GraftonTrent H. WellsJian-Xin Qi
- Cited by
- Developmental NeuroscienceComputer Vision and Pattern RecognitionCellular and Molecular Neuroscience
- Journals
- New England Journal of Medicine (1 paper)IEEE Transactions on Pattern Analysis and Machine Intelligence (2 papers)IEEE Transactions on Geoscience and Remote Sensing (1 paper)
- Partner nations
- ChinaUnited StatesHong Kong
In The Last Decade
Siyuan Huang
57 papers receiving 1.9k citations
Hit Papers
Peers
Comparison fields: 5 of 140
- Developmental Neuroscience 175
- Computer Vision and Pattern Recognition 621
- Cellular and Molecular Neuroscience 432
- Neurology 299
- Geology 98
Countries citing papers authored by Siyuan Huang
This map shows the geographic impact of Siyuan Huang'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 Siyuan Huang with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Siyuan Huang more than expected).
Fields of papers citing papers by Siyuan Huang
This network shows the impact of papers produced by Siyuan Huang. 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 Siyuan Huang. The network helps show where Siyuan Huang may publish in the future.
Co-authorship network
The 25 scholars most cited alongside Siyuan Huang, 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 | 0 | |
| 2 | 2025 | 0 | |
| 3 | 2025 | 1 | |
| 4 | 2025 | 0 | |
| 5 | 2025 | 1 | |
| 6 | 2025 | 0 | |
| 7 | 2025 | 1 | |
| 8 | 2024 | 4 | |
| 9 | 2024 | 17 | |
| 10 | 2024 | 13 | |
| 11 | 2024 | 4 | |
| 12 | 2024 | 3 | |
| 13 | 2024 | 5 | |
| 14 | 2024 | 5 | |
| 15 | 2023 | 25 | |
| 16 | 2021 | 2 | |
| 17 | 2021 | 19 | |
| 18 | 2021 | 43 | |
| 19 | 2020 | 19 | |
| 20 | 2020 | 77 |
About Siyuan Huang
Siyuan Huang is a scholar working on Computer Vision and Pattern Recognition, Human-Computer Interaction and Geology, having authored 68 papers that have together received 2.0k indexed citations. Recurring topics across this work include Multimodal Machine Learning Applications (14 papers), Human Pose and Action Recognition (11 papers), Medical Imaging Techniques and Applications (7 papers), Natural Language Processing Techniques (6 papers), Robot Manipulation and Learning (5 papers), 3D Surveying and Cultural Heritage (5 papers), Speech and dialogue systems (4 papers) and Topic Modeling (4 papers). The work is most often cited by research in Developmental Neuroscience (175 citations), Computer Vision and Pattern Recognition (621 citations) and Cellular and Molecular Neuroscience (432 citations). Siyuan Huang has collaborated with scholars based in China, United States and Hong Kong. Frequent co-authors include Song‐Chun Zhu, Yixin Zhu, Siyuan Qi, John C. Mazziotta, Stuart A. Schneck, Scott T. Grafton, Trent H. Wells, Jian-Xin Qi, Lorraine O. Ramig and Jeffrey A. Snyder. Their work appears in journals such as New England Journal of Medicine, IEEE Transactions on Pattern Analysis and Machine Intelligence and IEEE Transactions on Geoscience and Remote Sensing.
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.