Junxian Li: h-index, Total Citations, and Citation Map
Junxian Li's h-index is 7 (6 i10-index, 260+ total citations across 23+ publications) according to Google Scholar as of May 2026. Junxian Li is affiliated with Shanghai Jiao Tong University.
Junxian Li is a researcher affiliated with Shanghai Jiao Tong University, specializing in MLLM & science MLLM, model efficiency, CV. Their work has been cited 260 times. This profile visualizes their global influence, highlighting strong citation networks in China.
Junxian Li's Citation Metrics
Bibliometric impact based on 23 indexed publications.
- H-Index
- 7
- i10-Index
- 6
- Total Citations
- 260
- Citing Countries
- 24
As of May 2026.
Junxian Li has an h-index of 7 and 260 total citations across 23 publications, with research cited by institutions in 24 countries.
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Global Impact Map
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Top Cited Works
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Chemvlm: Exploring the power of multimodal large language models in chemistry area
202590
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.
The researcher pioneered multimodal large language models for chemistry and subsequently investigated efficiency vulnerabilities in VLM-based GUI agents, establishing a foundational line of inquiry in specialized AI applications.
The researcher developed reinforcement learning methods for traffic signal control that remain effective despite missing data, addressing a critical robustness gap in intelligent transportation systems.
The researcher developed Critic-V, a framework using VLM critics to detect errors in multimodal reasoning, establishing a novel approach to improving model reliability.
Citation trend (last 10 years)Click to expand
Citation Trend (Last 10 Years)
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