Chelsea Finn: h-index, Total Citations, and Citation Map
Chelsea Finn's h-index is 126 (274 i10-index, 126,630+ total citations across 348+ publications) according to Google Scholar as of May 2026. Chelsea Finn is affiliated with Stanford University, Physical Intelligence.
Chelsea Finn is a researcher affiliated with Stanford University, Physical Intelligence, specializing in machine learning, robotics, reinforcement learning. Their work has been cited 126,630 times. This profile visualizes their global influence, highlighting strong citation networks in China.
Chelsea Finn's Citation Metrics
Bibliometric impact based on 348 indexed publications.
- H-Index
- 126
- i10-Index
- 274
- Total Citations
- 126,630
- Citing Countries
- 40
As of May 2026.
Chelsea Finn has an h-index of 126 and 126,630 total citations across 348 publications, with research cited by institutions in 40 countries.
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We've mapped 5,000 of 126,630 citations for Chelsea Finn
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Global Impact Map
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Top Cited Works
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Model-agnostic meta-learning for fast adaptation of deep networks
201719,589
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.
527 citing papers could not be classified (no author data) — excluded from the percentages above.
The researcher pioneered model-agnostic meta-learning for fast adaptation and subsequently analyzed the systemic opportunities and risks of foundation models.
The researcher developed model-agnostic meta-learning, a foundational framework enabling deep networks to rapidly adapt to new tasks with minimal data, establishing a standard for efficient few-shot learning.
The researcher introduced Direct Preference Optimization, a seminal framework revealing that language models inherently function as reward models, thereby simplifying alignment processes.
Citation trend (last 10 years)Click to expand
Citation Trend (Last 10 Years)
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