Jiahao Xu: h-index, Total Citations, and Citation Map
Jiahao Xu's h-index is 8 (6 i10-index, 198+ total citations across 16+ publications) according to Google Scholar as of May 2026. Jiahao Xu is affiliated with University of Nevada, Reno.
Jiahao Xu is a researcher affiliated with University of Nevada, Reno, specializing in Generative models, Federated Learning. Their work has been cited 198 times. This profile visualizes their global influence, highlighting strong citation networks in United States.
Jiahao Xu's Citation Metrics
Bibliometric impact based on 16 indexed publications. Of these, 15 are original research articles — the rest are literature highlights, conference abstracts or theses.
- H-Index
- 8
- i10-Index
- 6
- Total Citations
- 198
- Citing Countries
- 20
As of May 2026.
Jiahao Xu has an h-index of 8 and 198 total citations across 16 publications, with research cited by institutions in 20 countries.
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Global Impact Map
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Top Cited Works
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Federated Learning for Smart Grid: A Survey on Applications and Potential Vulnerabilities
202459
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.
104 citing papers could not be classified (no author data) — excluded from the percentages above.
The researcher pioneered data-efficient GAN training via architectural reconfiguration, establishing a foundational framework subsequently extended to medical imaging alignment and dynamic pruning techniques.
The researcher developed a direction alignment inspection method to detect backdoor attacks in federated learning, subsequently extending this framework to include traceable black-box watermarks for model provenance.
The researcher established a foundational survey framework for federated learning in smart grids, systematically mapping applications and identifying critical security vulnerabilities.
Citation trend (last 10 years)Click to expand
Citation Trend (Last 10 Years)
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