Ming Zhou (周明): h-index, Total Citations, and Citation Map
Ming Zhou (周明)'s h-index is 1 (1 i10-index, 69,448+ total citations across 1+ publications) according to Google Scholar as of June 2026. Ming Zhou (周明) is affiliated with Chief Scientist at Sinovation, ACL president (2019), VP of CCF(2020-2024).
Ming Zhou (周明) is a researcher affiliated with Chief Scientist at Sinovation, ACL president (2019), VP of CCF(2020-2024), specializing in AI & Machine Learning. Their work has been cited 69,448 times. This profile visualizes their global influence, highlighting strong citation networks in China.
Ming Zhou (周明)'s Citation Metrics
Bibliometric impact based on 1 indexed publication.
- H-Index
- 1
- i10-Index
- 1
- Total Citations
- 69,448
- Citing Countries
- 6
As of June 2026.
Ming Zhou (周明) has an h-index of 1 and 69,448 total citations across 1 publication, with research cited by institutions in 6 countries.
Global Impact Map
Visualizing the geographic distribution of institutions that have cited your work.
Starting…
Pins will appear here as institutions are resolved — no need to refresh.
CodeBERT: A Pre-Trained Model for Programming and Natural Languages
20204,789
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.
Citation trend (last 10 years)Click to expand
Citation Trend (Last 10 Years)
Related Guides
Learn how to use citation maps for your research and visa applications.
About Ming Zhou (周明)'s research
Ming Zhou (周明) is a researcher in AI & Machine Learning at Chief Scientist at Sinovation, ACL president (2019), VP of CCF(2020-2024). Their work has been cited 69,448 times across 1 publications, according to Google Scholar.
Their most-cited work, “CodeBERT: A Pre-Trained Model for Programming and Natural Languages” (2020), has accumulated 4,789 citations.
Citations of Ming Zhou (周明)'s research come primarily from China, Australia and Canada, reflecting international research impact across 5+ countries. The interactive citation map above shows the full geographic distribution of the institutions citing this work.
