S Kornblith: h-index, Total Citations, and Citation Map
S Kornblith's h-index is 20 (20 i10-index, 63,016+ total citations across 20+ publications) according to Google Scholar as of September 2026. S Kornblith is affiliated with Google DeepMind.
S Kornblith is a researcher affiliated with Google DeepMind, specializing in various fields. Their work has been cited 63,016 times. This profile visualizes their global influence, highlighting strong citation networks in China.
S Kornblith's Citation Metrics
Bibliometric impact based on 20 indexed publications.
- H-Index
- 20
- i10-Index
- 20
- Total Citations
- 63,016
- Citing Countries
- 58
As of September 2026.
S Kornblith has an h-index of 20 and 63,016 total citations across 20 publications, with research cited by institutions in 58 countries.
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Global Impact Map
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A Simple Framework for Contrastive Learning of Visual Representations
20207,315
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.
11 citing papers could not be classified (no author data) — excluded from the percentages above.
The researcher established a foundational framework for understanding scene processing in the macaque temporal lobe, subsequently extending these neural coding principles to human parahippocampal cortex.
The researcher developed a foundational framework for contrastive learning of visual representations, establishing a standard approach that has been widely adopted across the computer vision community.
The researcher established that hierarchical processing in the human medial temporal lobe is indicated by the latency and selectivity of single neurons.
Citation trend (last 10 years)Click to expand
Citation Trend (Last 10 Years)
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About S Kornblith's research
S Kornblith is a researcher at Google DeepMind. Their work has been cited 63,016 times across 20 publications (h-index 20), according to Google Scholar.
Their most-cited work, “A Simple Framework for Contrastive Learning of Visual Representations” (2020), has accumulated 7,315 citations. Other influential works include “Do Better ImageNet Models Transfer Better?” (2019) with 1,225 citations and “When Does Label Smoothing Help?” (2019) with 884 citations.
Citations of S Kornblith'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.
