Qingyi Lu: h-index, Total Citations, and Citation Map
Qingyi Lu's h-index is 8 (8 i10-index, 173+ total citations across 10+ publications) according to Google Scholar as of May 2026. Qingyi Lu is affiliated with Brown University.
Qingyi Lu is a researcher affiliated with Brown University, specializing in Computer Science, Machine learning, Large Language Model. Their work has been cited 173 times. This profile visualizes their global influence, highlighting strong citation networks in United States.
Qingyi Lu's Citation Metrics
Bibliometric impact based on 10 indexed publications.
- H-Index
- 8
- i10-Index
- 8
- Total Citations
- 173
- Citing Countries
- 12
As of May 2026.
Qingyi Lu has an h-index of 8 and 173 total citations across 10 publications, with research cited by institutions in 12 countries.
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Global Impact Map
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Top Cited Works
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Research on adaptive algorithm recommendation system based on parallel data mining platform
202428
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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.
53 citing papers could not be classified (no author data) — excluded from the percentages above.
The researcher developed a multi-modal clothing recommendation framework using large models and VAEs, subsequently extending this methodology to short-video systems with differential privacy and LLM content detection.
The researcher advanced recommendation systems by integrating collaborative filtering with large language models, a novel approach that has garnered significant independent scholarly attention.
The researcher established a foundational framework for analyzing user privacy preferences in large language models, specifically addressing the critical challenge of deriving insights from limited data.
Citation trend (last 10 years)Click to expand
Citation Trend (Last 10 Years)
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