Chelsea Finn Google Scholar: h-index, Total Citations, and Citation Map
Chelsea Finn's h-index is 131 (283 i10-index, 141,410+ total citations across 363+ publications) according to Google Scholar as of October 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 141,410 times. This profile visualizes their global influence, highlighting strong citation networks in China.
Chelsea Finn's Citation Metrics
Bibliometric impact based on 363 indexed publications.
- H-Index
- 131
- i10-Index
- 283
- Total Citations
- 141,410
- Citing Countries
- 74
As of October 2026.
Chelsea Finn has an h-index of 131 and 141,410 total citations across 363 publications, with research cited by institutions in 74 countries.
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Global Impact Map
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Model-agnostic meta-learning for fast adaptation of deep networks
201720,406
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.
522 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 141,410 times across 363 publications (h-index 131), according to Google Scholar.
Their most-cited work, “Model-agnostic meta-learning for fast adaptation of deep networks” (2017), has accumulated 20,406 citations. Other influential works include “Model-agnostic meta-learning for fast adaptation of deep networks” (2017) with 20,406 citations and “On the opportunities and risks of foundation models” (2021) with 11,330 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.
