Alec Radford: h-index, Total Citations, and Citation Map
Alec Radford's h-index is 51 (64 i10-index, 357,875+ total citations across 29+ publications) according to Google Scholar as of June 2026. Alec Radford is affiliated with Independent.
Alec Radford is a researcher affiliated with Independent, specializing in Deep Learning, Machine Learning. Their work has been cited 357,875 times. This profile visualizes their global influence, highlighting strong citation networks in United States.
Alec Radford's Citation Metrics
Bibliometric impact based on 29 indexed publications.
- H-Index
- 51
- i10-Index
- 64
- Total Citations
- 357,875
- Citing Countries
- 31
As of June 2026.
Alec Radford has an h-index of 51 and 357,875 total citations across 29 publications, with research cited by institutions in 31 countries.
Global Impact Map
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Language Models are Few-Shot Learners
202071,423
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.
The researcher advanced language model capabilities through a seminal 2019 study and subsequent high-impact technical reports, establishing a foundational trajectory for modern AI development.
The researcher advanced reinforcement learning and language model alignment by releasing open-source baselines and pioneering human preference fine-tuning, establishing foundational tools adopted by the broader scientific community.
The researcher advanced generative AI by developing foundational models for music synthesis and zero-shot text-to-image generation, establishing a highly cited trajectory in multimodal content creation.
Citation trend (last 10 years)Click to expand
Citation Trend (Last 10 Years)
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About Alec Radford's research
Alec Radford is a researcher in Deep Learning and Machine Learning at Independent. Their work has been cited 357,875 times across 29 publications (h-index 51), according to Google Scholar.
Their most-cited work, “Language Models are Few-Shot Learners” (2020), has accumulated 71,423 citations. Other influential works include “Learning Transferable Visual Models From Natural Language Supervision” (2021) with 60,432 citations and “Proximal policy optimization algorithms” (2017) with 38,645 citations.
Citations of Alec Radford'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.
