Ilya Sutskever: h-index, Total Citations, and Citation Map
Ilya Sutskever's h-index is 101 (158 i10-index, 795,418+ total citations across 199+ publications) according to Google Scholar as of May 2026. Ilya Sutskever is affiliated with Co-Founder and Chief Scientist at Safe Superintelligence Inc.
Ilya Sutskever is a researcher affiliated with Co-Founder and Chief Scientist at Safe Superintelligence Inc, specializing in Machine Learning, Neural Networks, Artificial Intelligence. Their work has been cited 795,418 times. This profile visualizes their global influence, highlighting strong citation networks in United States.
Ilya Sutskever's Citation Metrics
Bibliometric impact based on 199 indexed publications.
- H-Index
- 101
- i10-Index
- 158
- Total Citations
- 795,418
- Citing Countries
- 64
As of May 2026.
Ilya Sutskever has an h-index of 101 and 795,418 total citations across 199 publications, with research cited by institutions in 64 countries.
Global Impact Map
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Top Cited Works
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ImageNet Classification with Deep Convolutional Neural Networks
2012194,282
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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.
544 citing papers could not be classified (no author data) — excluded from the percentages above.
The researcher pioneered the application of deep convolutional neural networks to large-scale image classification, establishing a foundational architecture that significantly advanced the field of computer vision.
The researcher introduced Dropout, a simple regularization technique that prevents neural networks from overfitting, establishing a foundational method for improving model generalization in deep learning.
The researcher demonstrated that large-scale language models can perform complex tasks with minimal examples, establishing few-shot learning as a foundational capability in modern AI.
Citation trend (last 10 years)Click to expand
Citation Trend (Last 10 Years)
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