Gang Hua Google Scholar: h-index, Total Citations, and Citation Map
Gang Hua's h-index is 85 (260 i10-index, 30,087+ total citations across 412+ publications) according to Google Scholar as of October 2026. Gang Hua is affiliated with Director of Applied Science, AI, Amazon.com, Inc., IEEE & IAPR Fellow.
Gang Hua is a researcher affiliated with Director of Applied Science, AI, Amazon.com, Inc., IEEE & IAPR Fellow, specializing in various fields. Their work has been cited 30,087 times. This profile visualizes their global influence, highlighting strong citation networks in China.
Gang Hua's Citation Metrics
Bibliometric impact based on 412 indexed publications. Of these, 19 are original research articles — the rest are literature highlights, conference abstracts or theses.
- H-Index
- 85
- i10-Index
- 260
- Total Citations
- 30,087
- Citing Countries
- 75
As of October 2026.
Gang Hua has an h-index of 85 and 30,087 total citations across 412 publications, with research cited by institutions in 75 countries.
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Global Impact Map
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A convolutional neural network cascade for face detection
20151,891
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.
6 citing papers could not be classified (no author data) — excluded from the percentages above.
The researcher advanced neural image processing by developing explicit style representations and extending these methods to dehazing, deraining, and identity-preserving face synthesis.
The researcher developed a foundational sparse graph convolutional framework for pedestrian trajectory prediction, subsequently extending this approach to unified transformer architectures and robust multi-object tracking systems.
The researcher developed discriminant embedding methods for local image descriptors, establishing a foundational framework for learning robust visual features that significantly advanced computer vision.
Citation trend (last 10 years)Click to expand
Citation Trend (Last 10 Years)
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About Gang Hua's research
Gang Hua is a researcher at Director of Applied Science, AI, Amazon.com, Inc., IEEE & IAPR Fellow. Their work has been cited 30,087 times across 412 publications (h-index 85), according to Google Scholar.
Their most-cited work, “A convolutional neural network cascade for face detection” (2015), has accumulated 1,891 citations. Other influential works include “Stacked cross attention for image-text matching” (2018) with 1,844 citations and “Lq-nets: Learned quantization for highly accurate and compact deep neural networks” (2018) with 1,070 citations.
Citations of Gang Hua's research come primarily from China, United States and United Kingdom, reflecting international research impact across 5+ countries. The interactive citation map above shows the full geographic distribution of the institutions citing this work.
