Carl Doersch: h-index, Total Citations, and Citation Map
Carl Doersch's h-index is 33 (39 i10-index, 34,053+ total citations across 57+ publications) according to Google Scholar as of August 2026. Carl Doersch is affiliated with Google DeepMind.
Carl Doersch is a researcher affiliated with Google DeepMind, specializing in Computer Vision, Machine Learning. Their work has been cited 34,053 times. This profile visualizes their global influence, highlighting strong citation networks in China.
Carl Doersch's Citation Metrics
Bibliometric impact based on 57 indexed publications.
- H-Index
- 33
- i10-Index
- 39
- Total Citations
- 34,053
- Citing Countries
- 58
As of August 2026.
Carl Doersch has an h-index of 33 and 34,053 total citations across 57 publications, with research cited by institutions in 58 countries.
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Global Impact Map
Visualizing the geographic distribution of institutions that have cited your work.
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Bootstrap Your Own Latent-A New Approach to Self-Supervised Learning
202011,233
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.
8 citing papers could not be classified (no author data) — excluded from the percentages above.
The researcher developed a foundational framework for tracking arbitrary points with per-frame initialization, subsequently extending it to robust training and robotic imitation tasks.
The researcher introduced a novel self-supervised learning framework, Bootstrap Your Own Latent, which has become a seminal reference in the field with over 11,000 citations.
The researcher pioneered unsupervised visual representation learning via context prediction, establishing a foundational framework that has been widely adopted by the independent research community.
Citation trend (last 10 years)Click to expand
Citation Trend (Last 10 Years)
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About Carl Doersch's research
Carl Doersch is a researcher in Computer Vision and Machine Learning at Google DeepMind. Their work has been cited 34,053 times across 57 publications (h-index 33), according to Google Scholar.
Their most-cited work, “Bootstrap Your Own Latent-A New Approach to Self-Supervised Learning” (2020), has accumulated 11,233 citations. Other influential works include “Gemini 2.5: Pushing the frontier with advanced reasoning, multimodality, long context, and next generation agentic capabilities” (2025) with 4,069 citations and “Unsupervised visual representation learning by context prediction” (2015) with 3,876 citations.
Citations of Carl Doersch'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.
