Daniel Gedon: h-index, Total Citations, and Citation Map
Daniel Gedon's h-index is 9 (9 i10-index, 536+ total citations across 3+ publications) according to Google Scholar as of May 2026. Daniel Gedon is affiliated with Postdoc, Tübingen University.
Daniel Gedon is a researcher affiliated with Postdoc, Tübingen University, specializing in machine learning, scientific discovery, simulation-based inference. Their work has been cited 536 times. This profile visualizes their global influence, highlighting strong citation networks in Brazil.
Daniel Gedon's Citation Metrics
Bibliometric impact based on 3 indexed publications. Of these, 2 are original research articles — the rest are literature highlights, conference abstracts or theses.
- H-Index
- 9
- i10-Index
- 9
- Total Citations
- 536
- Citing Countries
- 16
As of May 2026.
Daniel Gedon has an h-index of 9 and 536 total citations across 3 publications, with research cited by institutions in 16 countries.
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Global Impact Map
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Top Cited Works
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Deep networks for system identification: a Survey
2025164
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 developed and validated a deep learning model to predict myocardial infarction from ECGs in emergency department patients, establishing a novel diagnostic approach.
The researcher developed a deep neural network framework for screening Chagas disease using electrocardiograms, establishing a novel, non-invasive diagnostic approach published in a high-impact journal.
The researcher provided a comprehensive survey of deep networks for system identification, establishing a foundational reference in Automatica with substantial independent scholarly uptake.
Citation trend (last 10 years)Click to expand
Citation Trend (Last 10 Years)
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