Boqing Gong: h-index, Total Citations, and Citation Map
Boqing Gong's h-index is 63 (109 i10-index, 28,392+ total citations across 168+ publications) according to Google Scholar as of July 2026. Boqing Gong is affiliated with Boston University, Google.
Boqing Gong is a researcher affiliated with Boston University, Google, specializing in Machine Learning, Computer Vision. Their work has been cited 28,392 times. This profile visualizes their global influence, spanning a global audience.
Boqing Gong's Citation Metrics
Bibliometric impact based on 168 indexed publications.
- H-Index
- 63
- i10-Index
- 109
- Total Citations
- 28,392
- Citing Countries
- 0
As of July 2026.
Boqing Gong has an h-index of 63 and 28,392 total citations across 168 publications, with research cited by institutions in 0 countries.
Download Exports (PNG, CSV, Poster)
Free Viewing Boqing Gong's citation map is always free. Pay once to download poster, PNG, and CSV files for offline use or your visa packet.
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.
Gemini 2.5: Pushing the frontier with advanced reasoning, multimodality, long context, and next generation agentic capabilities
20254,042
Top Citing Countries
Top Citing Institutions
No institution data available.
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 Boqing Gong's research
Boqing Gong is a researcher in Machine Learning and Computer Vision at Boston University, Google. Their work has been cited 28,392 times across 168 publications (h-index 63), according to Google Scholar.
Their most-cited work, “Gemini 2.5: Pushing the frontier with advanced reasoning, multimodality, long context, and next generation agentic capabilities” (2025), has accumulated 4,042 citations. Other influential works include “Geodesic Flow Kernel for Unsupervised Domain Adaptation” (2012) with 3,190 citations and “Geodesic flow kernel for unsupervised domain adaptation” (2012) with 2,187 citations.











