Joelle Pineau: h-index, Total Citations, and Citation Map
Joelle Pineau's h-index is 85 (232 i10-index, 43,737+ total citations across 412+ publications) according to Google Scholar as of September 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 43,737 times. This profile visualizes their global influence, spanning a global audience.
Joelle Pineau's Citation Metrics
Bibliometric impact based on 412 indexed publications. Of these, 17 are original research articles — the rest are literature highlights, conference abstracts or theses.
- H-Index
- 85
- i10-Index
- 232
- Total Citations
- 43,737
- Citing Countries
- 0
As of September 2026.
Joelle Pineau has an h-index of 85 and 43,737 total citations across 412 publications, with research cited by institutions in 0 countries.
Download Exports (PNG, CSV, Poster)
Free Viewing Joelle Pineau'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.
Deep reinforcement learning that matters
20183,524
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 Joelle Pineau's research
Joelle Pineau is a researcher in Artificial intelligence, Machine learning and Robotics at School of Computer Science, McGill University; FAIR, Meta AI; Mila. Their work has been cited 43,737 times across 412 publications (h-index 85), according to Google Scholar.
Their most-cited work, “Deep reinforcement learning that matters” (2018), has accumulated 3,524 citations. Other influential works include “An introduction to deep reinforcement learning” (2018) with 2,547 citations and “Building end-to-end dialogue systems using generative hierarchical neural network models” (2016) with 2,281 citations.











