Fumito Uwano (上野史): h-index, Total Citations, and Citation Map
Fumito Uwano (上野史)'s h-index is 7 (4 i10-index, 177+ total citations across 91+ publications) according to Google Scholar as of May 2026. Fumito Uwano (上野史) is affiliated with Tokyo University of Agriculture and Technology.
Fumito Uwano (上野史) is a researcher affiliated with Tokyo University of Agriculture and Technology, specializing in Multi-agent Learning. Their work has been cited 177 times. This profile visualizes their global influence, highlighting strong citation networks in Japan.
Fumito Uwano (上野史)'s Citation Metrics
Bibliometric impact based on 91 indexed publications.
- H-Index
- 7
- i10-Index
- 4
- Total Citations
- 177
- Citing Countries
- 16
As of May 2026.
Fumito Uwano (上野史) has an h-index of 7 and 177 total citations across 91 publications, with research cited by institutions in 16 countries.
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Top Cited Works
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Real-Time Sleep Stage Estimation from Biological Data with Trigonometric Function Regression Model.
201628
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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.
53 citing papers could not be classified (no author data) — excluded from the percentages above.
The researcher developed a reinforcement learning framework for multi-agent cooperation using internal rewards, extending it to handle heterogeneous inputs and non-communicative cyclic environments.
The researcher developed a trigonometric regression model for real-time sleep stage estimation, establishing a methodological foundation for subsequent analyses of heart rate data in well-being computing.
The researcher developed a modified cuckoo search algorithm for dynamic optimization, establishing a framework for adapting to randomly changing environments through subsequent methodological refinements.
Citation trend (last 10 years)Click to expand
Citation Trend (Last 10 Years)
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