Chenghao Wang: h-index, Total Citations, and Citation Map
Chenghao Wang's h-index is 4 (4 i10-index, 159+ total citations across 7+ publications) according to Google Scholar as of May 2026. Chenghao Wang is affiliated with University of Michigan, Ann Arbor.
Chenghao Wang is a researcher affiliated with University of Michigan, Ann Arbor, specializing in control, robotics, dynamics. Their work has been cited 159 times. This profile visualizes their global influence, highlighting strong citation networks in China.
Chenghao Wang's Citation Metrics
Bibliometric impact based on 7 indexed publications.
- H-Index
- 4
- i10-Index
- 4
- Total Citations
- 159
- Citing Countries
- 19
As of May 2026.
Chenghao Wang has an h-index of 4 and 159 total citations across 7 publications, with research cited by institutions in 19 countries.
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Global Impact Map
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Top Cited Works
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A comprehensive study on the locomotion characteristics of a metameric earthworm-like robot: Part B: Gait analysis and experiments
201570
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.
8 citing papers could not be classified (no author data) — excluded from the percentages above.
The researcher established a foundational framework for peristaltic locomotion in multi-segment in-pipe robots, advancing the field through rigorous gait analysis and experimental validation.
The researcher advanced computer vision efficiency by developing CUDA-accelerated implementations of scale-invariant feature transforms, enabling high-performance processing widely adopted by independent scholars.
The researcher developed a neural radiance field method to convert 2D images into 3D textures, establishing a foundational approach for 3D reconstruction from 2D data.
Citation trend (last 10 years)Click to expand
Citation Trend (Last 10 Years)
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