Geoffrey Hinton: h-index, Total Citations, and Citation Map
Geoffrey Hinton's h-index is 194 (547 i10-index, 1,080,894+ total citations across 829+ publications) according to Google Scholar as of September 2026. Geoffrey Hinton is affiliated with Emeritus Prof. Computer Science, University of Toronto.
Geoffrey Hinton is a researcher affiliated with Emeritus Prof. Computer Science, University of Toronto, specializing in machine learning, psychology, artificial intelligence. Their work has been cited 1,080,894 times. This profile visualizes their global influence, highlighting strong citation networks in United States.
Geoffrey Hinton's Citation Metrics
Bibliometric impact based on 829 indexed publications.
- H-Index
- 194
- i10-Index
- 547
- Total Citations
- 1,080,894
- Citing Countries
- 113
As of September 2026.
Geoffrey Hinton has an h-index of 194 and 1,080,894 total citations across 829 publications, with research cited by institutions in 113 countries.
Global Impact Map
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ImageNet classification with deep convolutional neural networks
2012198,566
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.
2982 citing papers could not be classified (no author data) — excluded from the percentages above.
The researcher pioneered foundational error-propagation methods for internal representations, establishing the theoretical and empirical basis for modern deep learning architectures.
The researcher pioneered deep convolutional neural networks for image classification and advanced visual representation learning through contrastive frameworks and normalization techniques.
Citation trend (last 10 years)Click to expand
Citation Trend (Last 10 Years)
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About Geoffrey Hinton's research
Geoffrey Hinton is a researcher in machine learning, psychology and artificial intelligence at Emeritus Prof. Computer Science, University of Toronto. Their work has been cited 1,080,894 times across 829 publications (h-index 194), according to Google Scholar.
Their most-cited work, “ImageNet classification with deep convolutional neural networks” (2012), has accumulated 198,566 citations. Other influential works include “Deep learning” (2015) with 117,439 citations and “Visualizing High-Dimensional Data Using t-SNE” (2008) with 70,348 citations.
Citations of Geoffrey Hinton's research come primarily from United States, China 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.
