Joelle Pineau
About
Appointed Canada CIFAR AI Chair – 2018
Renewed Canada CIFAR AI Chair – 2023
Joëlle Pineau is a Professor and William Dawson Scholar at the School of Computer Science at McGill University, where she co-directs the Reasoning and Learning Lab. She is a core academic member of Mila and a Canada CIFAR AI Chair. She is also a VP, AI research at Meta (previously Facebook), where she leads the Fundamental AI Research (FAIR) team.
Pineau’s research focuses on developing new models and algorithms for planning and learning in complex partially-observable domains. She also works on applying these algorithms to complex problems in robotics, health care, games and conversational agents. She serves on the editorial board of the Journal of Machine Learning Research and is Past-President of the International Machine Learning Society.
Awards
- AI 2000 Most Influential Scholars, AAAI/IJCAI, 2023
- Member, Royal Society of Canada, 2023
- Tartans on the Rise, Carnegie Mellon University, 2023
- Governor General's Innovation Awards, 2019
- NSERC E.W.R. Steacie Memorial Fellowship, 2018
- Facebook Research Award, 2017
- Member of the College of New Scholars, Artists and Scientists, Royal Society of Canada, 2016
- William Dawson Scholar, McGill University, 2015
Relevant Publications
- Srikumar, M., et al. (2023). Advancing ethics review practices in AI research. Nature Machine Intelligence (Vol. 4 No.12).
- Sachan, D.S., Lewis, M., Yogatama, D., Zettlemoyer, L., Pineau, J., & Zaheer, M. (2023). Questions are all you need to train a dense passage retriever. Transactions of the Association for Computational Linguistics (Vol. 11).
- Sachan, D.S., et al. (2022). Improving passage retrieval with zero-shot question generation.
- Yarats, D. Zhang, A., Kostrikov, I., Amos, B., Pineau, J., & Fergus R. (2021). Improving sample efficiency in model-free reinforcement learning from images. Proceedings of the AAAI Conference on Artificial Intelligence (Vol. 35, No. 12).
Henderson, P., Islam, R., Bachman, P., Pineau, J., Precup, D., & Meger, D. (2018). Deep reinforcement learning that matters. In Proceedings of the AAAI conference on artificial intelligence (Vol. 32, No. 1).