Siming Yan
Impact in
- Cognitive Neuroscience top 10%
- Neural dynamics and brain function
- Face Recognition and Perception
- Visual perception and processing mechanisms
- Functional Brain Connectivity Studies
Papers in
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- Image Processing and 3D Reconstruction 3
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- 3D Shape Modeling and Analysis 3
- Co-authors
- Daniel Yamins (2 shared papers)Aran Nayebi (2 shared papers)Chengxu Zhuang (2 shared papers)Martin Schrimpf (1 shared paper)Michael C. Frank (1 shared paper)James J. DiCarlo (1 shared paper)Qixing Huang (4 shared papers)Zhenpei Yang (3 shared papers)
- Journals
- Computers & Graphics (1 paper)Proceedings of the National Academy of Sciences (1 paper)International Joint Conference on Natural Language Processing (1 paper)2021 IEEE/CVF International Conference on Computer Vision (ICCV) (2 papers)
- Partner nations
- United StatesChinaSouth Korea
In The Last Decade
Siming Yan
9 papers receiving 241 citations
Peers
Comparison fields: 5 of 48
- Cognitive Neuroscience 143
- Computer Graphics and Computer-Aided Design 17
- Computer Vision and Pattern Recognition 85
- Geology 22
- Biophysics 14
Countries citing papers authored by Siming Yan
This map shows the geographic impact of Siming Yan'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 Siming Yan with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Siming Yan more than expected).
Fields of papers citing papers by Siming Yan
This network shows the impact of papers produced by Siming Yan. 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 Siming Yan. The network helps show where Siming Yan may publish in the future.
Co-authors
The 25 scholars most cited alongside Siming Yan, 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 | 2021 | 169 | |
| 2 | 2021 | 25 | |
| 3 | 2020 | 19 | |
| 4 | 2021 | 15 | |
| 5 | 2018 | 6 | |
| 6 | 2013 | 4 | |
| 7 | 2023 | 3 | |
| 8 | Leveraging Diverse Lexical Chains to Construct Essays for Chinese College Entrance Examination | 2017 | 2 |
| 9 | 2019 | 2 |
About Siming Yan
Siming Yan is a scholar working on Computer Vision and Pattern Recognition, Computational Mechanics, Geology, Cognitive Neuroscience and Control and Systems Engineering, having authored 9 papers that have together received 245 indexed citations. Recurring topics across this work include Image Processing and 3D Reconstruction (3 papers), 3D Shape Modeling and Analysis (3 papers), 3D Surveying and Cultural Heritage (3 papers), Face Recognition and Perception (2 papers), Neural dynamics and brain function (2 papers), Cardiac Imaging and Diagnostics (1 paper), Natural Language Processing Techniques (1 paper) and Robotics and Sensor-Based Localization (1 paper). The work is most often cited by research in Cognitive Neuroscience (143 citations), Computer Graphics and Computer-Aided Design (17 citations), Computer Vision and Pattern Recognition (85 citations), Geology (22 citations) and Biophysics (14 citations). Siming Yan has collaborated with scholars based in United States, China and South Korea. Frequent co-authors include Daniel Yamins, Aran Nayebi, Chengxu Zhuang, Martin Schrimpf, Michael C. Frank, James J. DiCarlo, Qixing Huang, Zhenpei Yang, Chongyang Ma and Haibin Huang. Their work appears in journals such as Computers & Graphics, Proceedings of the National Academy of Sciences, International Joint Conference on Natural Language Processing and 2021 IEEE/CVF International Conference on Computer Vision (ICCV).
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.