Haoran Li: h-index, Total Citations, and Citation Map
Haoran Li's h-index is 5 (5 i10-index, 423+ total citations across 7+ publications) according to Google Scholar as of May 2026. Haoran Li is affiliated with Machine Learning Department, Carnegie Mellon University.
Haoran Li is a researcher affiliated with Machine Learning Department, Carnegie Mellon University, specializing in Machine Learning, Foundation Models, Reinforcement Learning. Their work has been cited 423 times. This profile visualizes their global influence, highlighting strong citation networks in China.
Haoran Li's Citation Metrics
Bibliometric impact based on 7 indexed publications.
- H-Index
- 5
- i10-Index
- 5
- Total Citations
- 423
- Citing Countries
- 27
As of May 2026.
Haoran Li has an h-index of 5 and 423 total citations across 7 publications, with research cited by institutions in 27 countries.
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Global Impact Map
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Top Cited Works
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MMed-RAG: Versatile Multimodal RAG System for Medical Vision Language Models
2025197
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.
82 citing papers could not be classified (no author data) — excluded from the percentages above.
The researcher developed a reliable multimodal RAG framework to enhance factuality in medical vision-language models, establishing a foundational approach for accurate clinical AI reasoning.
The researcher advanced event causality identification by developing a heuristic semantic dependency inquiry network, establishing a novel methodological framework for analyzing causal relationships in textual data.
Citation trend (last 10 years)Click to expand
Citation Trend (Last 10 Years)
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