Doina Precup: h-index, Total Citations, and Citation Map
Doina Precup's h-index is 82 (295 i10-index, 50,954+ total citations across 598+ publications) according to Google Scholar as of July 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 50,954 times. This profile visualizes their global influence, highlighting strong citation networks in United States.
Doina Precup's Citation Metrics
Bibliometric impact based on 598 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
- 50,954
- Citing Countries
- 58
As of July 2026.
Doina Precup has an h-index of 82 and 50,954 total citations across 598 publications, with research cited by institutions in 58 countries.
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Global Impact Map
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The multimodal brain tumor image segmentation benchmark (BRATS)
20148,845
Top Citing Countries
Top Citing Institutions
Visa Evidence Package
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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.
19 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)
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About Doina Precup's research
Doina Precup is a researcher at DeepMind and McGill University. Their work has been cited 50,954 times across 598 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.
