Ming Wang: h-index, Total Citations, and Citation Map
Ming Wang's h-index is 7 (6 i10-index, 557+ total citations across 18+ publications) according to Google Scholar as of May 2026. Ming Wang is affiliated with Ph.D. student of Singapore Management University & Northeastern University.
Ming Wang is a researcher affiliated with Ph.D. student of Singapore Management University & Northeastern University, specializing in Machine Psychology, AI for Mental Health, LLM-based Agents. Their work has been cited 557 times. This profile visualizes their global influence, highlighting strong citation networks in China.
Ming Wang's Citation Metrics
Bibliometric impact based on 18 indexed publications. Of these, 17 are original research articles — the rest are literature highlights, conference abstracts or theses.
- H-Index
- 7
- i10-Index
- 6
- Total Citations
- 557
- Citing Countries
- 35
As of May 2026.
Ming Wang has an h-index of 7 and 557 total citations across 18 publications, with research cited by institutions in 35 countries.
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Global Impact Map
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Top Cited Works
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Is mamba effective for time series forecasting?
2025379
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.
303 citing papers could not be classified (no author data) — excluded from the percentages above.
The researcher critically evaluated the efficacy of Mamba architectures for time series forecasting, establishing a foundational benchmark that has garnered significant independent scholarly attention.
The researcher developed LangGPT, a structured reusable prompt design framework for LLMs that rethinks prompt engineering through the lens of programming language principles.
The researcher developed MM-InstructEval, a framework for zero-shot evaluation of multimodal large language models on reasoning tasks, establishing a benchmark for assessing model capabilities without task-specific training.
Citation trend (last 10 years)Click to expand
Citation Trend (Last 10 Years)
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