Samuel Ritter Google Scholar: h-index, Total Citations, and Citation Map
Samuel Ritter's h-index is 13 (14 i10-index, 8,365+ total citations across 24+ publications) according to Google Scholar as of November 2026. Samuel Ritter is affiliated with DeepMind.
Samuel Ritter is a researcher affiliated with DeepMind, specializing in Machine Learning, Neuroscience, Cognitive Science. Their work has been cited 8,365 times. This profile visualizes their global influence, highlighting strong citation networks in China.
Samuel Ritter's Citation Metrics
Bibliometric impact based on 24 indexed publications.
- H-Index
- 13
- i10-Index
- 14
- Total Citations
- 8,365
- Citing Countries
- 20
As of November 2026.
Samuel Ritter has an h-index of 13 and 8,365 total citations across 24 publications, with research cited by institutions in 20 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.
Gemini 2.5: Pushing the frontier with advanced reasoning, multimodality, long context, and next generation agentic capabilities
20255,597
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 Samuel Ritter's research
Samuel Ritter is a researcher in Machine Learning, Neuroscience and Cognitive Science at DeepMind. Their work has been cited 8,365 times across 24 publications (h-index 13), 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 5,597 citations. Other influential work includes “Reinforcement learning, fast and slow” (2019) with 1,026 citations.
Citations of Samuel Ritter'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.
