Richard Zhang is a researcher affiliated with Senior Research Scientist, Adobe, specializing in AI & Machine Learning. Their work has been cited 46,547 times. This profile visualizes their global influence, highlighting strong citation networks in CN.
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Bibliometric impact based on 1 indexed publication.
As of April 2026.
Richard Zhang has an h-index of 1 and 46,547 total citations across 1 publication, with research cited by institutions in 2 countries.
Visualizing the geographic distribution of institutions that have cited your work.
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.
Visa-ready visualisation: each outer ring node is a country citing this scholar. Node size & edge weight scale with citing-paper count. Showing top 2 of 2 countries (20 total citations).
Ranked by EB-1A / O-1A relevance: independence from your home institution, affiliation with a top-ranked institution, repeat citations of your work, and recency. Reach out to the highest-scoring candidates to request support letters.
Jie Tang
7.0Tsinghua University
China· Cited your work 1 time
Wenyi Hong
7.0Tsinghua University
China· Cited your work 1 time
Zhaofeng Zhang
8.5University of Michigan
USA· Cited your work 1 time
Da Yin
7.0Tsinghua University
China· Cited your work 1 time
Weihan Wang
7.0Tsinghua University
China· Cited your work 1 time
Yean Cheng
7.0Tsinghua University
China· Cited your work 1 time
Yuxiao Dong
7.0Tsinghua University
China· Cited your work 1 time
Zhuoyi Yang
7.0Tsinghua University
China· Cited your work 1 time
Jiayan Teng
7.0Tsinghua University
China· Cited your work 1 time
Wendi Zheng
7.0Tsinghua University
China· Cited your work 1 time
Scores combine 5 signals: independence (+3), prestige institution (+2), repeat citations (+2 per paper, cap 5), recent activity (+1), and geography diversity (+1.5). Identity matching uses name + institution; namesakes at the same org are merged.
Citations flagged as non-independent share the scholar's home institution (University of California, Berkeley). EB-1A & O-1A petitions typically quote the independent-citation count as the stronger evidence of outside recognition.
· cites “The Unreasonable Effectiveness of Deep Features as a Perceptual Metric”
Banghao Chen, Zhaofeng Zhang, Nicolas Langrené, Shengxin Zhu
Patterns· cites “The Unreasonable Effectiveness of Deep Features as a Perceptual Metric”
Shiyu Huang, Yuanming Yang, Wenyi Hong, Da Yin + 14 more
International Conference on Learning Representations (ICLR) 2025· cites “The Unreasonable Effectiveness of Deep Features as a Perceptual Metric”
· cites “The Unreasonable Effectiveness of Deep Features as a Perceptual Metric”
· cites “The Unreasonable Effectiveness of Deep Features as a Perceptual Metric”
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