neta shaul: h-index, Total Citations, and Citation Map
neta shaul's h-index is 8 (8 i10-index, 935+ total citations across 5+ publications) according to Google Scholar as of June 2026. neta shaul is affiliated with Computer Science PhD student, Weizmann Institute of Science.
neta shaul is a researcher affiliated with Computer Science PhD student, Weizmann Institute of Science, specializing in Deep Learning, Generative Models. Their work has been cited 935 times. This profile visualizes their global influence, highlighting strong citation networks in United States.
neta shaul's Citation Metrics
Bibliometric impact based on 5 indexed publications.
- H-Index
- 8
- i10-Index
- 8
- Total Citations
- 935
- Citing Countries
- 5
As of June 2026.
neta shaul has an h-index of 8 and 935 total citations across 5 publications, with research cited by institutions in 5 countries.
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Discrete Flow Matching
2024322
Top Citing Countries
Top Citing Institutions
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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 advanced generative modeling and decision-making through guided flows, a contribution evidenced by a seminal 2023 paper with 97 citations from entirely independent researchers.
The researcher introduced Discrete Flow Matching, a novel generative modeling framework published at NeurIPS 2024 that has achieved significant independent scholarly adoption.
The researcher provided a foundational guide and code for flow matching, establishing a widely adopted resource that has garnered significant independent scholarly attention.
Citation trend (last 10 years)Click to expand
Citation Trend (Last 10 Years)
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About neta shaul's research
neta shaul is a researcher in Deep Learning and Generative Models at Computer Science PhD student, Weizmann Institute of Science. Their work has been cited 935 times across 5 publications (h-index 8), according to Google Scholar.
Their most-cited work, “Discrete Flow Matching” (2024), has accumulated 322 citations. Other influential works include “Flow Matching Guide and Code” (2024) with 308 citations and “Guided Flows for Generative Modeling and Decision Making” (2023) with 97 citations.
Citations of neta shaul's research come primarily from United States, China and Canada, reflecting international research impact across 5+ countries. The interactive citation map above shows the full geographic distribution of the institutions citing this work.
