Lei Chen: h-index, Total Citations, and Citation Map
Lei Chen's h-index is 94 (609 i10-index, 38,460+ total citations across 5+ publications) according to Google Scholar as of May 2026. Lei Chen is affiliated with Hong Kong University of Science and Technology.
Lei Chen is a researcher affiliated with Hong Kong University of Science and Technology, specializing in Human Powered Machine Learning, Databases, Data Mining. Their work has been cited 38,460 times. This profile visualizes their global influence, highlighting strong citation networks in United States.
Lei Chen's Citation Metrics
Bibliometric impact based on 5 indexed publications.
- H-Index
- 94
- i10-Index
- 609
- Total Citations
- 38,460
- Citing Countries
- 1
As of May 2026.
Lei Chen has an h-index of 94 and 38,460 total citations across 5 publications, with research cited by institutions in 1 country.
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We've mapped 5,000 of 38,460 citations for Lei Chen
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Global Impact Map
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Top Cited Works
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Robust and fast similarity search for moving object trajectories
20051,857
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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.
The researcher developed robust and fast similarity search methods for moving object trajectories, establishing a foundational approach in spatial-temporal data analysis.
The researcher established a foundational theoretical framework integrating lp-norms with edit distance, creating a seminal reference point for metric analysis in computational research.
The researcher developed K-automorphism, a general framework for privacy-preserving network publication, establishing a foundational approach to balancing data utility with privacy protection in graph publishing.
Citation trend (last 10 years)Click to expand
Citation Trend (Last 10 Years)
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