Emma Brunskill: h-index, Total Citations, and Citation Map
Emma Brunskill's h-index is 70 (170 i10-index, 29,341+ total citations across 293+ publications) according to Google Scholar as of September 2026. Emma Brunskill is affiliated with Associate Professor of Computer Science, Stanford University.
Emma Brunskill is a researcher affiliated with Associate Professor of Computer Science, Stanford University, specializing in Reinforcement Learning, Machine Learning, Decision Making Under Uncertainty. Their work has been cited 29,341 times. This profile visualizes their global influence, spanning a global audience.
Emma Brunskill's Citation Metrics
Bibliometric impact based on 293 indexed publications.
- H-Index
- 70
- i10-Index
- 170
- Total Citations
- 29,341
- Citing Countries
- 0
As of September 2026.
Emma Brunskill has an h-index of 70 and 29,341 total citations across 293 publications, with research cited by institutions in 0 countries.
Download Exports (PNG, CSV, Poster)
Free Viewing Emma Brunskill'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.
On the opportunities and risks of foundation models
202111,603
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 Emma Brunskill's research
Emma Brunskill is a researcher in Reinforcement Learning, Machine Learning and Decision Making Under Uncertainty at Associate Professor of Computer Science, Stanford University. Their work has been cited 29,341 times across 293 publications (h-index 70), according to Google Scholar.
Their most-cited work, “On the opportunities and risks of foundation models” (2021), has accumulated 11,603 citations. Other influential works include “Towards the systematic reporting of the energy and carbon footprints of machine learning” (2020) with 1,210 citations and “Global and regional hearing impairment prevalence: an analysis of 42 studies in 29 countries” (2013) with 1,015 citations.
