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 June 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 June 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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Action Matching: A Variational Method for Learning Stochastic Dynamics from Samples
2023120
Top Citing Countries
Top Citing Institutions
Visa Evidence Package
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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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About Daniel Severo's research
Daniel Severo is a researcher in Information Theory, Machine Learning and Generative Modelling at Meta - FAIR Labs. Their work has been cited 374 times across 5 publications (h-index 9), according to Google Scholar.
Their most-cited work, “Action Matching: A Variational Method for Learning Stochastic Dynamics from Samples” (2023), has accumulated 120 citations. Other influential works include “Accelerated Sampling from Masked Diffusion Models via Entropy Bounded Unmasking” (2025) with 68 citations and “Flow Matching with General Discrete Paths: A Kinetic-Optimal Perspective” (2024) with 47 citations.
Citations of Daniel Severo'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.
