Chelsea Finn: h-index, Total Citations, and Citation Map
Chelsea Finn's h-index is 128 (280 i10-index, 135,474+ total citations across 355+ publications) according to Google Scholar as of July 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 135,474 times. This profile visualizes their global influence, highlighting strong citation networks in China.
Chelsea Finn's Citation Metrics
Bibliometric impact based on 355 indexed publications.
- H-Index
- 128
- i10-Index
- 280
- Total Citations
- 135,474
- Citing Countries
- 65
As of July 2026.
Chelsea Finn has an h-index of 128 and 135,474 total citations across 355 publications, with research cited by institutions in 65 countries.
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We've mapped 5,000 of 135,474 citations for Chelsea Finn
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Global Impact Map
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Model-agnostic meta-learning for fast adaptation of deep networks
201720,061
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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About Chelsea Finn's research
Chelsea Finn is a researcher in machine learning, robotics and reinforcement learning at Stanford University, Physical Intelligence. Their work has been cited 135,474 times across 355 publications (h-index 128), according to Google Scholar.
Their most-cited work, “Model-agnostic meta-learning for fast adaptation of deep networks” (2017), has accumulated 20,061 citations. Other influential works include “Model-agnostic meta-learning for fast adaptation of deep networks” (2017) with 19,566 citations and “Direct Preference Optimization: Your Language Model is Secretly a Reward Model” (2023) with 10,612 citations.
Citations of Chelsea Finn'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.
