Peiyang Yu: h-index, Total Citations, and Citation Map
Peiyang Yu's h-index is 10 (10 i10-index, 539+ total citations across 12+ publications) according to Google Scholar as of May 2026. Peiyang Yu is affiliated with Carnegie Mellon Univeristy.
Peiyang Yu is a researcher affiliated with Carnegie Mellon Univeristy, specializing in Large Language Models, Fake News Detection, Misinformation Detection. Their work has been cited 539 times. This profile visualizes their global influence, highlighting strong citation networks in United States.
Peiyang Yu's Citation Metrics
Bibliometric impact based on 12 indexed publications. Of these, 11 are original research articles — the rest are literature highlights, conference abstracts or theses.
- H-Index
- 10
- i10-Index
- 10
- Total Citations
- 539
- Citing Countries
- 47
As of May 2026.
Peiyang Yu has an h-index of 10 and 539 total citations across 12 publications, with research cited by institutions in 47 countries.
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Top Cited Works
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Optimization of Transformer heart disease prediction model based on particle swarm optimization algorithm
2024130
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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.
The researcher developed optimized Transformer architectures for predictive modeling and misinformation detection, establishing a framework for enhancing model performance through advanced optimization techniques.
The researcher advanced NLP-based text classification and extended these methods to LLM-driven applications in fake news detection and recommendation systems.
The researcher developed a hybrid attention framework integrating large language models to enhance the accuracy and robustness of automated fake news detection systems.
Citation trend (last 10 years)Click to expand
Citation Trend (Last 10 Years)
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