Ziqing Wang: h-index, Total Citations, and Citation Map
Ziqing Wang's h-index is 12 (13 i10-index, 407+ total citations across 20+ publications) according to Google Scholar as of May 2026. Ziqing Wang is affiliated with Northwestern University.
Ziqing Wang is a researcher affiliated with Northwestern University, specializing in Efficient AI. Their work has been cited 407 times. This profile visualizes their global influence, highlighting strong citation networks in China.
Ziqing Wang's Citation Metrics
Bibliometric impact based on 20 indexed publications. Of these, 19 are original research articles — the rest are literature highlights, conference abstracts or theses.
- H-Index
- 12
- i10-Index
- 13
- Total Citations
- 407
- Citing Countries
- 11
As of May 2026.
Ziqing Wang has an h-index of 12 and 407 total citations across 20 publications, with research cited by institutions in 11 countries.
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Global Impact Map
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Top Cited Works
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Masked spiking transformer
2023129
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.
263 citing papers could not be classified (no author data) — excluded from the percentages above.
The researcher pioneered efficient spiking transformer architectures, establishing a foundational framework for energy-efficient neural processing that has been extended to autonomous driving and wavelet-based models.
The researcher pioneered spiking denoising diffusion models and developed a unified conversion framework for spiking neural networks, establishing a foundational approach for efficient neuromorphic generative AI.
The researcher developed AutoST, a training-free neural architecture search method for spiking transformers, establishing a novel approach to efficient model design in neuromorphic computing.
Citation trend (last 10 years)Click to expand
Citation Trend (Last 10 Years)
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