Daniel Severo: h-index, Total Citations, and Citation Map
Daniel Severo's h-index is 9 (8 i10-index, 374+ total citations across 5+ publications) according to Google Scholar as of May 2026. Daniel Severo is affiliated with Meta - FAIR Labs.
Daniel Severo is a researcher affiliated with Meta - FAIR Labs, specializing in Information Theory, Machine Learning, Generative Modelling. Their work has been cited 374 times. This profile visualizes their global influence, highlighting strong citation networks in China.
Daniel Severo's Citation Metrics
Bibliometric impact based on 5 indexed publications.
- H-Index
- 9
- i10-Index
- 8
- Total Citations
- 374
- Citing Countries
- 12
As of May 2026.
Daniel Severo has an h-index of 9 and 374 total citations across 5 publications, with research cited by institutions in 12 countries.
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Global Impact Map
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Top Cited Works
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Action Matching: A Variational Method for Learning Stochastic Dynamics from Samples
2023120
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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 developed Action Matching, a variational method for learning stochastic dynamics from samples, establishing a foundational framework for probabilistic modeling in dynamical systems.
The researcher developed methods for predicting multiple ICD-10 codes from Brazilian-Portuguese clinical notes, addressing a critical gap in multilingual medical NLP.
The researcher advanced lossless compression efficiency by introducing Monte Carlo bits-back coding, a technique that appears to improve compression rates through probabilistic modeling.
Citation trend (last 10 years)Click to expand
Citation Trend (Last 10 Years)
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