Yiqun Zhang: h-index, Total Citations, and Citation Map
Yiqun Zhang's h-index is 10 (10 i10-index, 279+ total citations across 18+ publications) according to Google Scholar as of May 2026. Yiqun Zhang is affiliated with Northeastern University, China; Shanghai Artificial Intelligence Laboratory.
Yiqun Zhang is a researcher affiliated with Northeastern University, China; Shanghai Artificial Intelligence Laboratory, specializing in empathetic dialogue, LLM-based agent, Multi-agent. Their work has been cited 279 times. This profile visualizes their global influence, highlighting strong citation networks in China.
Yiqun Zhang's Citation Metrics
Bibliometric impact based on 18 indexed publications. Of these, 15 are original research articles — the rest are literature highlights, conference abstracts or theses.
- H-Index
- 10
- i10-Index
- 10
- Total Citations
- 279
- Citing Countries
- 30
As of May 2026.
Yiqun Zhang has an h-index of 10 and 279 total citations across 18 publications, with research cited by institutions in 30 countries.
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Global Impact Map
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Top Cited Works
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Affective computing in the era of large language models: A survey from the nlp perspective
202662
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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.
107 citing papers could not be classified (no author data) — excluded from the percentages above.
The researcher pioneered multimodal empathetic response generation from scratch, establishing a foundational framework subsequently expanded to integrate large language model capabilities and tool-use mechanisms.
The researcher pioneered performance-efficiency optimized routing for LLMs, establishing a foundational framework subsequently expanded through comprehensive benchmarking and large-scale aggregation studies.
The researcher developed a routing recipe for collective intelligence in language models, establishing a framework for coordinated multi-agent reasoning.
Citation trend (last 10 years)Click to expand
Citation Trend (Last 10 Years)
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