Xiangyu Zhang: h-index, Total Citations, and Citation Map
Xiangyu Zhang's h-index is 79 (116 i10-index, 442,963+ total citations across 3+ publications) according to Google Scholar as of May 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 442,963 times. This profile visualizes their global influence, highlighting strong citation networks in United States.
Xiangyu Zhang's Citation Metrics
Bibliometric impact based on 3 indexed publications.
- H-Index
- 79
- i10-Index
- 116
- Total Citations
- 442,963
- Citing Countries
- 9
As of May 2026.
Xiangyu Zhang has an h-index of 79 and 442,963 total citations across 3 publications, with research cited by institutions in 9 countries.
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We've mapped 5,000 of 442,963 citations for Xiangyu Zhang
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Top Cited Works
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Deep Residual Learning for Image Recognition
2016316,706
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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.
2 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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