Li Fei-Fei: h-index, Total Citations, and Citation Map
Li Fei-Fei's h-index is 176 (445 i10-index, 347,517+ total citations across 701+ publications) according to Google Scholar as of July 2026. Li Fei-Fei is affiliated with Professor of Computer Science, Stanford University.
Li Fei-Fei is a researcher affiliated with Professor of Computer Science, Stanford University, specializing in Artificial Intelligence, Machine Learning, Computer Vision. Their work has been cited 347,517 times. This profile visualizes their global influence, highlighting strong citation networks in United States.
Li Fei-Fei's Citation Metrics
Bibliometric impact based on 701 indexed publications.
- H-Index
- 176
- i10-Index
- 445
- Total Citations
- 347,517
- Citing Countries
- 65
As of July 2026.
Li Fei-Fei has an h-index of 176 and 347,517 total citations across 701 publications, with research cited by institutions in 65 countries.
Global Impact Map
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ImageNet: A Large-Scale Hierarchical Image Database
200994,654
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.
932 citing papers could not be classified (no author data) — excluded from the percentages above.
The researcher established a foundational large-scale hierarchical image database and recognition challenge that catalyzed the development of modern computer vision and foundation models.
The researcher pioneered socially aware trajectory prediction models, establishing a foundational framework for human motion forecasting in crowded environments that has been widely adopted across computer vision and robotics.
The researcher developed a foundational framework for segmenting arbitrary structures in medical images, establishing a versatile tool that has become a standard reference in computational pathology and medical imaging analysis.
Citation trend (last 10 years)Click to expand
Citation Trend (Last 10 Years)
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About Li Fei-Fei's research
Li Fei-Fei is a researcher in Artificial Intelligence, Machine Learning and Computer Vision at Professor of Computer Science, Stanford University. Their work has been cited 347,517 times across 701 publications (h-index 176), according to Google Scholar.
Their most-cited work, “ImageNet: A Large-Scale Hierarchical Image Database” (2009), has accumulated 94,654 citations. Other influential works include “Imagenet large scale visual recognition challenge” (2015) with 54,986 citations and “Perceptual Losses for Real-Time Style Transfer and Super-Resolution” (2016) with 14,705 citations.
Citations of Li Fei-Fei'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.
