Antônio H. Ribeiro: h-index, Total Citations, and Citation Map
Antônio H. Ribeiro's h-index is 24 (44 i10-index, 54,654+ total citations across 5+ publications) according to Google Scholar as of May 2026. Antônio H. Ribeiro is affiliated with Assistant Professor, Uppsala University.
Antônio H. Ribeiro is a researcher affiliated with Assistant Professor, Uppsala University, specializing in Machine learning, Signal processing, Biomedical Informatics. Their work has been cited 54,654 times. This profile visualizes their global influence, highlighting strong citation networks in United States.
Antônio H. Ribeiro's Citation Metrics
Bibliometric impact based on 5 indexed publications. Of these, 4 are original research articles — the rest are literature highlights, conference abstracts or theses.
- H-Index
- 24
- i10-Index
- 44
- Total Citations
- 54,654
- Citing Countries
- 23
As of May 2026.
Antônio H. Ribeiro has an h-index of 24 and 54,654 total citations across 5 publications, with research cited by institutions in 23 countries.
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We've mapped 5,000 of 54,654 citations for Antônio H. Ribeiro
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Top Cited Works
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SciPy 1.0: fundamental algorithms for scientific computing in Python
202050,938
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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 established fundamental algorithms for scientific computing in Python, as evidenced by a seminal Nature Methods paper with over 50,000 citations.
The researcher developed a deep neural network framework for the automatic diagnosis of 12-lead ECGs, establishing a seminal benchmark in AI-driven cardiac signal analysis.
The researcher advanced RNN training theory by analyzing stability through attractors and smoothness, moving beyond gradient issues, as evidenced by a highly cited 2020 AISTATS paper.
Citation trend (last 10 years)Click to expand
Citation Trend (Last 10 Years)
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