Jeff Clune: h-index, Total Citations, and Citation Map
Jeff Clune's h-index is 65 (110 i10-index, 48,299+ total citations across 142+ publications) according to Google Scholar as of August 2026. Jeff Clune is affiliated with Professor, Computer Science,U British Columbia; CIFAR AI Chair, Vector Institute.
Jeff Clune is a researcher affiliated with Professor, Computer Science,U British Columbia; CIFAR AI Chair, Vector Institute, specializing in various fields. Their work has been cited 48,299 times. This profile visualizes their global influence, highlighting strong citation networks in United States.
Jeff Clune's Citation Metrics
Bibliometric impact based on 142 indexed publications.
- H-Index
- 65
- i10-Index
- 110
- Total Citations
- 48,299
- Citing Countries
- 80
As of August 2026.
Jeff Clune has an h-index of 65 and 48,299 total citations across 142 publications, with research cited by institutions in 80 countries.
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Global Impact Map
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How transferable are features in deep neural networks?
201413,641
Top Citing Countries
Top Citing Institutions
Visa Evidence Package
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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.
217 citing papers could not be classified (no author data) — excluded from the percentages above.
The researcher established foundational insights into deep neural network feature transferability and representation convergence, pioneering methods for understanding and visualizing learned features.
The researcher pioneered elite-mapping search strategies, establishing a foundational framework for automated system design and open-ended learning that has driven widespread independent adoption.
The researcher established foundational theories on the evolutionary origins of modularity and advanced neuroevolution methods, significantly influencing biological sciences and machine intelligence through highly cited, independent scholarly uptake.
Citation trend (last 10 years)Click to expand
Citation Trend (Last 10 Years)
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About Jeff Clune's research
Jeff Clune is a researcher at Professor, Computer Science,U British Columbia; CIFAR AI Chair, Vector Institute. Their work has been cited 48,299 times across 142 publications (h-index 65), according to Google Scholar.
Their most-cited work, “How transferable are features in deep neural networks?” (2014), has accumulated 13,641 citations. Other influential works include “Deep Neural Networks are Easily Fooled: High Confidence Predictions for Unrecognizable Images” (2015) with 5,019 citations and “Understanding Neural Networks Through Deep Visualization” (2015) with 2,773 citations.
Citations of Jeff Clune'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.
