Ekin Dogus Cubuk: h-index, Total Citations, and Citation Map
Ekin Dogus Cubuk's h-index is 61 (103 i10-index, 50,821+ total citations across 198+ publications) according to Google Scholar as of July 2026. Ekin Dogus Cubuk is affiliated with Google DeepMind.
Ekin Dogus Cubuk is a researcher affiliated with Google DeepMind, specializing in Machine Learning, Materials Theory, Materials Discovery. Their work has been cited 50,821 times. This profile visualizes their global influence, highlighting strong citation networks in United States.
Ekin Dogus Cubuk's Citation Metrics
Bibliometric impact based on 198 indexed publications.
- H-Index
- 61
- i10-Index
- 103
- Total Citations
- 50,821
- Citing Countries
- 24
As of July 2026.
Ekin Dogus Cubuk has an h-index of 61 and 50,821 total citations across 198 publications, with research cited by institutions in 24 countries.
Download Exports (PNG, CSV, Poster)
Free Viewing Ekin Dogus Cubuk's citation map is always free. Pay once to download poster, PNG, and CSV files for offline use or your visa packet.
Global Impact Map
Visualizing the geographic distribution of institutions that have cited your work.
Starting…
Pins will appear here as institutions are resolved — no need to refresh.
Fixmatch: Simplifying semi-supervised learning with consistency and confidence
20206,443
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.
4 citing papers could not be classified (no author data) — excluded from the percentages above.
The researcher developed RandAugment, a practical automated data augmentation method with a reduced search space, significantly advancing efficient image preprocessing techniques.
The researcher developed SpecAugment, a simple yet highly influential data augmentation method that significantly advanced automatic speech recognition performance.
The researcher developed AutoAugment, a method for learning data augmentation strategies directly from data, establishing a foundational approach in automated machine learning.
Citation trend (last 10 years)Click to expand
Citation Trend (Last 10 Years)
Related Guides
Learn how to use citation maps for your research and visa applications.
About Ekin Dogus Cubuk's research
Ekin Dogus Cubuk is a researcher in Machine Learning, Materials Theory and Materials Discovery at Google DeepMind. Their work has been cited 50,821 times across 198 publications (h-index 61), according to Google Scholar.
Their most-cited work, “Fixmatch: Simplifying semi-supervised learning with consistency and confidence” (2020), has accumulated 6,443 citations. Other influential works include “Randaugment: Practical automated data augmentation with a reduced search space” (2020) with 5,655 citations and “Specaugment: A simple data augmentation method for automatic speech recognition” (2019) with 5,601 citations.
Citations of Ekin Dogus Cubuk'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.
