Leto Peel: h-index, Total Citations, and Citation Map
Leto Peel's h-index is 16 (21 i10-index, 2,461+ total citations across 33+ publications) according to Google Scholar as of May 2026. Leto Peel is affiliated with Maastricht University.
Leto Peel is a researcher affiliated with Maastricht University, specializing in Machine Learning, Complex Networks, Complex Systems. Their work has been cited 2,461 times. This profile visualizes their global influence, highlighting strong citation networks in United States.
Leto Peel's Citation Metrics
Bibliometric impact based on 33 indexed publications. Of these, 15 are original research articles — the rest are literature highlights, conference abstracts or theses.
- H-Index
- 16
- i10-Index
- 21
- Total Citations
- 2,461
- Citing Countries
- 14
As of May 2026.
Leto Peel has an h-index of 16 and 2,461 total citations across 33 publications, with research cited by institutions in 14 countries.
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Global Impact Map
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Top Cited Works
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The ground truth about metadata and community detection in networks
2017620
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 established a foundational framework for evaluating metadata's role in network community detection, as evidenced by a highly cited 2017 Science Advances paper.
The researcher developed a data-driven prognostics framework integrating Kalman filter ensembles with neural networks, establishing a foundational approach for hybrid predictive modeling in engineering systems.
The researcher pioneered the application of Gaussian process active learning to maritime anomaly detection, establishing a foundational framework for efficient surveillance in complex oceanic environments.
Citation trend (last 10 years)Click to expand
Citation Trend (Last 10 Years)
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