Xiangyu Zhang: h-index, Total Citations, and Citation Map
Xiangyu Zhang's h-index is 80 (115 i10-index, 463,092+ total citations across 134+ publications) according to Google Scholar as of September 2026. Xiangyu Zhang is affiliated with Co-founder & Chief Scientist of StepFun.
Xiangyu Zhang is a researcher affiliated with Co-founder & Chief Scientist of StepFun, specializing in Neural Network Architectures, Efficient Deep Learning, Computer Vision. Their work has been cited 463,092 times. This profile visualizes their global influence, highlighting strong citation networks in China.
Xiangyu Zhang's Citation Metrics
Bibliometric impact based on 134 indexed publications.
- H-Index
- 80
- i10-Index
- 115
- Total Citations
- 463,092
- Citing Countries
- 63
As of September 2026.
Xiangyu Zhang has an h-index of 80 and 463,092 total citations across 134 publications, with research cited by institutions in 63 countries.
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Global Impact Map
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Deep Residual Learning for Image Recognition
2016330,909
Top Citing Countries
Top Citing Institutions
Visa Evidence Package
Views and exports tuned for EB-1A, O-1A, and EB-2 NIW petitions. Sustained acclaim, geographic reach, and independent-citation filtering are the strongest evidence categories immigration adjudicators look for.
Significant Contributions
Auto-detected research lines — a seminal paper and the follow-up work building on it. Review and edit before using in a petition. Each Free PDF opens in a new tab — EB-1A organises this into the structure USCIS applies to Criterion 5 of 8 CFR § 204.5(h)(3)(v); EB-1B re-frames it under § 204.5(i)(3) (outstanding researcher); NIW presents it under prong 2 of Matter of Dhanasar.
1 citing papers could not be classified (no author data) — excluded from the percentages above.
The researcher published a seminal 2015 paper that has garnered over 30,000 citations, establishing a foundational contribution widely adopted by independent scholars across the field.
The researcher introduced Spatial Pyramid Pooling to enable fixed-length representations in deep convolutional networks for visual recognition, a foundational technique widely adopted by independent scholars.
The researcher introduced deep residual learning for image recognition, a foundational approach that significantly advanced deep neural network training and performance in computer vision.
Citation trend (last 10 years)Click to expand
Citation Trend (Last 10 Years)
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About Xiangyu Zhang's research
Xiangyu Zhang is a researcher in Neural Network Architectures, Efficient Deep Learning and Computer Vision at Co-founder & Chief Scientist of StepFun. Their work has been cited 463,092 times across 134 publications (h-index 80), according to Google Scholar.
Their most-cited work, “Deep Residual Learning for Image Recognition” (2016), has accumulated 330,909 citations. Other influential works include “2015 publication — 31218 citations” (2015) with 31,218 citations and “Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition” (2015) with 19,602 citations.
Citations of Xiangyu Zhang's research come primarily from China, United States and United Kingdom, reflecting international research impact across 5+ countries. The interactive citation map above shows the full geographic distribution of the institutions citing this work.
