Andrew Dai Google Scholar: h-index, Total Citations, and Citation Map
Andrew Dai's h-index is 58 (88 i10-index, 77,207+ total citations across 137+ publications) according to Google Scholar as of October 2026. Andrew Dai is affiliated with Google DeepMind.
Andrew Dai is a researcher affiliated with Google DeepMind, specializing in Language models, Machine learning, Sequence models. Their work has been cited 77,207 times. This profile visualizes their global influence, highlighting strong citation networks in United States.
Andrew Dai's Citation Metrics
Bibliometric impact based on 137 indexed publications.
- H-Index
- 58
- i10-Index
- 88
- Total Citations
- 77,207
- Citing Countries
- 79
As of October 2026.
Andrew Dai has an h-index of 58 and 77,207 total citations across 137 publications, with research cited by institutions in 79 countries.
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Global Impact Map
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Palm: Scaling language modeling with pathways
20239,697
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.
32 citing papers could not be classified (no author data) — excluded from the percentages above.
Citation trend (last 10 years)Click to expand
Citation Trend (Last 10 Years)
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About Andrew Dai's research
Andrew Dai is a researcher in Language models, Machine learning and Sequence models at Google DeepMind. Their work has been cited 77,207 times across 137 publications (h-index 58), according to Google Scholar.
Their most-cited work, “Palm: Scaling language modeling with pathways” (2023), has accumulated 9,697 citations. Other influential works include “Gemini: a family of highly capable multimodal models” (2023) with 9,311 citations and “Scaling instruction-finetuned language models” (2024) with 6,376 citations.
Citations of Andrew Dai'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.
