Joelle Pineau: h-index, Total Citations, and Citation Map
Joelle Pineau's h-index is 84 (230 i10-index, 42,871+ total citations across 408+ publications) according to Google Scholar as of June 2026. Joelle Pineau is affiliated with School of Computer Science, McGill University; FAIR, Meta AI; Mila.
Joelle Pineau is a researcher affiliated with School of Computer Science, McGill University; FAIR, Meta AI; Mila, specializing in Artificial intelligence, Machine learning, Robotics. Their work has been cited 42,871 times. This profile visualizes their global influence, highlighting strong citation networks in United States.
Joelle Pineau's Citation Metrics
Bibliometric impact based on 408 indexed publications. Of these, 17 are original research articles — the rest are literature highlights, conference abstracts or theses.
- H-Index
- 84
- i10-Index
- 230
- Total Citations
- 42,871
- Citing Countries
- 74
As of June 2026.
Joelle Pineau has an h-index of 84 and 42,871 total citations across 408 publications, with research cited by institutions in 74 countries.
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Deep reinforcement learning that matters
20183,359
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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.
251 citing papers could not be classified (no author data) — excluded from the percentages above.
The researcher advanced deep reinforcement learning by establishing foundational principles and addressing critical challenges in sample efficiency and environmental sustainability.
The researcher pioneered end-to-end generative hierarchical neural models for dialogue systems, establishing a foundational framework that significantly advanced the field of conversational AI.
The researcher developed a seminal anytime algorithm for Partially Observable Markov Decision Processes, establishing a foundational point-based approach that significantly advanced computational methods in sequential decision-making under uncertainty.
Citation trend (last 10 years)Click to expand
Citation Trend (Last 10 Years)
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