Yanping Huang: h-index, Total Citations, and Citation Map
Yanping Huang's h-index is 32 (39 i10-index, 41,965+ total citations across 69+ publications) according to Google Scholar as of July 2026.
Yanping Huang is a researcher affiliated with their institution, specializing in Artificial Intelligence, Deep Learning, Machine Learning Systems. Their work has been cited 41,965 times. This profile visualizes their global influence, highlighting strong citation networks in China.
Yanping Huang's Citation Metrics
Bibliometric impact based on 69 indexed publications.
- H-Index
- 32
- i10-Index
- 39
- Total Citations
- 41,965
- Citing Countries
- 71
As of July 2026.
Yanping Huang has an h-index of 32 and 41,965 total citations across 69 publications, with research cited by institutions in 71 countries.
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Global Impact Map
Visualizing the geographic distribution of institutions that have cited your work.
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Gemini: a family of highly capable multimodal models
20239,329
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.
350 citing papers could not be classified (no author data) — excluded from the percentages above.
Citation trend (last 10 years)Click to expand
Citation Trend (Last 10 Years)
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About Yanping Huang's research
Yanping Huang is a researcher working on Artificial Intelligence, Deep Learning and Machine Learning Systems. Their work has been cited 41,965 times across 69 publications (h-index 32), according to Google Scholar.
Their most-cited work, “Gemini: a family of highly capable multimodal models” (2023), has accumulated 9,329 citations. Other influential works include “Scaling instruction-finetuned language models” (2024) with 6,187 citations and “Regularized evolution for image classifier architecture search” (2019) with 4,370 citations.
Citations of Yanping Huang'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.
