Eli Shechtman: h-index, Total Citations, and Citation Map
Eli Shechtman's h-index is 83 (164 i10-index, 67,675+ total citations across 3+ publications) according to Google Scholar as of May 2026. Eli Shechtman is affiliated with Senior Principal Scientist, Adobe Research.
Eli Shechtman is a researcher affiliated with Senior Principal Scientist, Adobe Research, specializing in Computer Vision, Computer Graphics, Machine Learning. Their work has been cited 67,675 times. This profile visualizes their global influence, highlighting strong citation networks in United States.
Eli Shechtman's Citation Metrics
Bibliometric impact based on 3 indexed publications.
- H-Index
- 83
- i10-Index
- 164
- Total Citations
- 67,675
- Citing Countries
- 6
As of May 2026.
Eli Shechtman has an h-index of 83 and 67,675 total citations across 3 publications, with research cited by institutions in 6 countries.
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Global Impact Map
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Top Cited Works
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The Unreasonable Effectiveness of Deep Features as a Perceptual Metric
201821,275
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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.
The researcher introduced a foundational framework for modeling human actions as space-time shapes, establishing a seminal approach in computer vision that has been widely adopted by the independent research community.
The researcher established deep neural network features as a robust perceptual metric for image quality, fundamentally shifting how visual similarity is quantified in computer vision.
The researcher advanced multimodal image-to-image translation, establishing a foundational framework that has been widely adopted by the independent research community.
Citation trend (last 10 years)Click to expand
Citation Trend (Last 10 Years)
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