Kaiming He Google Scholar: h-index, Total Citations, and Citation Map
Kaiming He's h-index is 78 (95 i10-index, 882,362+ total citations across 101+ publications) according to Google Scholar as of October 2026. Kaiming He is affiliated with Associate Professor, EECS, MIT.
Kaiming He is a researcher affiliated with Associate Professor, EECS, MIT, specializing in Computer Vision, Machine Learning. Their work has been cited 882,362 times. This profile visualizes their global influence, highlighting strong citation networks in China.
Kaiming He's Citation Metrics
Bibliometric impact based on 101 indexed publications.
- H-Index
- 78
- i10-Index
- 95
- Total Citations
- 882,362
- Citing Countries
- 81
As of October 2026.
Kaiming He has an h-index of 78 and 882,362 total citations across 101 publications, with research cited by institutions in 81 countries.
Global Impact Map
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Deep Residual Learning for Image Recognition
2016323,059
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.
1216 citing papers could not be classified (no author data) — excluded from the percentages above.
The researcher pioneered region proposal networks for real-time object detection, establishing a foundational framework that enabled subsequent breakthroughs in residual learning and instance segmentation.
The researcher developed Focal Loss, a novel loss function that addresses class imbalance in dense object detection, significantly improving model performance and establishing a new standard in the field.
The researcher established the Dark Channel Prior as a foundational method for single image haze removal, creating a seminal framework that has become a standard reference in computational photography.
Citation trend (last 10 years)Click to expand
Citation Trend (Last 10 Years)
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About Kaiming He's research
Kaiming He is a researcher in Computer Vision and Machine Learning at Associate Professor, EECS, MIT. Their work has been cited 882,362 times across 101 publications (h-index 78), according to Google Scholar.
Their most-cited work, “Deep Residual Learning for Image Recognition” (2016), has accumulated 323,059 citations. Other influential works include “Deep Residual Learning for Image Recognition” (2016) with 323,059 citations and “Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks” (2015) with 105,043 citations.
Citations of Kaiming He'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.
