Pan Zhang: h-index, Total Citations, and Citation Map
Pan Zhang's h-index is 73 (322 i10-index, 23,433+ total citations across 606+ publications) according to Google Scholar as of June 2026. Pan Zhang is affiliated with New York university.
Pan Zhang is a researcher affiliated with New York university, specializing in Perceptual learning. Their work has been cited 23,433 times. This profile visualizes their global influence, highlighting strong citation networks in China.
Pan Zhang's Citation Metrics
Bibliometric impact based on 606 indexed publications.
- H-Index
- 73
- i10-Index
- 322
- Total Citations
- 23,433
- Citing Countries
- 39
As of June 2026.
Pan Zhang has an h-index of 73 and 23,433 total citations across 606 publications, with research cited by institutions in 39 countries.
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We've mapped 5,000 of 23,433 citations for Pan Zhang
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Global Impact Map
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Sharegpt4v: Improving large multi-modal models with better captions
20241,195
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.
23 citing papers could not be classified (no author data) — excluded from the percentages above.
The researcher advanced large multi-modal models by introducing ShareGPT4V, a framework that leverages improved captions to enhance model performance, establishing a significant benchmark in the field.
The researcher advanced spectral methods for clustering sparse networks, establishing a foundational framework that has been widely adopted by independent scholars.
The researcher critically re-evaluates standard assessment protocols for large vision-language models, establishing a foundational framework that has garnered significant independent scholarly attention.
Citation trend (last 10 years)Click to expand
Citation Trend (Last 10 Years)
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