Doina Precup Google Scholar: h-index, Total Citations, and Citation Map
Doina Precup's h-index is 82 (295 i10-index, 53,260+ total citations across 100+ publications) according to Google Scholar as of October 2026. Doina Precup is affiliated with DeepMind and McGill University.
Doina Precup is a researcher affiliated with DeepMind and McGill University, specializing in various fields. Their work has been cited 53,260 times. This profile visualizes their global influence, highlighting strong citation networks in United States.
Doina Precup's Citation Metrics
Bibliometric impact based on 100 indexed publications. Of these, 19 are original research articles — the rest are literature highlights, conference abstracts or theses.
- H-Index
- 82
- i10-Index
- 295
- Total Citations
- 53,260
- Citing Countries
- 75
As of October 2026.
Doina Precup has an h-index of 82 and 53,260 total citations across 100 publications, with research cited by institutions in 75 countries.
Download Exports (PNG, CSV, Poster)
Free Viewing Doina Precup'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.
The multimodal brain tumor image segmentation benchmark (BRATS)
20148,845
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.
Significant Contributions
Auto-detected research lines — a seminal paper and the follow-up work building on it. Review and edit before using in a petition. Each Free PDF opens in a new tab — EB-1A organises this into the structure USCIS applies to Criterion 5 of 8 CFR § 204.5(h)(3)(v); EB-1B re-frames it under § 204.5(i)(3) (outstanding researcher); NIW presents it under prong 2 of Matter of Dhanasar.
13 citing papers could not be classified (no author data) — excluded from the percentages above.
The researcher established a foundational multimodal brain tumor segmentation benchmark and advanced the field by integrating uncertainty measures into deep learning models for lesion detection.
The researcher established foundational metrics for finite Markov decision processes, subsequently extending this framework to continuous domains, creating a widely adopted theoretical basis for system analysis.
The researcher established a foundational framework for temporal abstraction in reinforcement learning, bridging the gap between Markov Decision Processes and semi-Markov Decision Processes.
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 Doina Precup's research
Doina Precup is a researcher at DeepMind and McGill University. Their work has been cited 53,260 times across 100 publications (h-index 82), according to Google Scholar.
Their most-cited work, “The multimodal brain tumor image segmentation benchmark (BRATS)” (2014), has accumulated 8,845 citations. Other influential works include “Between MDPs and semi-MDPs: A framework for temporal abstraction in reinforcement learning” (1999) with 5,718 citations and “Deep reinforcement learning that matters” (2018) with 3,473 citations.
Citations of Doina Precup's research come primarily from United States, China 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.
