Ziad Obermeyer: h-index, Total Citations, and Citation Map
Ziad Obermeyer's h-index is 48 (70 i10-index, 25,971+ total citations across 110+ publications) according to Google Scholar as of July 2026. Ziad Obermeyer is affiliated with UC Berkeley.
Ziad Obermeyer is a researcher affiliated with UC Berkeley, specializing in Machine learning, medicine, public policy. Their work has been cited 25,971 times. This profile visualizes their global influence, highlighting strong citation networks in United States.
Ziad Obermeyer's Citation Metrics
Bibliometric impact based on 110 indexed publications. Of these, 16 are original research articles — the rest are literature highlights, conference abstracts or theses.
- H-Index
- 48
- i10-Index
- 70
- Total Citations
- 25,971
- Citing Countries
- 63
As of July 2026.
Ziad Obermeyer has an h-index of 48 and 25,971 total citations across 110 publications, with research cited by institutions in 63 countries.
Download Exports (PNG, CSV, Poster)
Free Viewing Ziad Obermeyer'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.
Dissecting racial bias in an algorithm used to manage the health of populations
20199,937
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 Ziad Obermeyer's research
Ziad Obermeyer is a researcher in Machine learning, medicine and public policy at UC Berkeley. Their work has been cited 25,971 times across 110 publications (h-index 48), according to Google Scholar.
Their most-cited work, “Dissecting racial bias in an algorithm used to manage the health of populations” (2019), has accumulated 9,937 citations. Other influential works include “Predicting the future—big data, machine learning, and clinical medicine” (2016) with 5,232 citations and “Prediction policy problems” (2015) with 996 citations.
Citations of Ziad Obermeyer's research come primarily from United States, United Kingdom and China, reflecting international research impact across 5+ countries. The interactive citation map above shows the full geographic distribution of the institutions citing this work.
