Samira Ebrahimi Kahou
About
Appointed Canada CIFAR AI Chair – 2020
Samira Ebrahimi Kahou is a Canada CIFAR AI Chair at Mila, an assistant professor at the Electrical and Software Engineering Department at the University of Calgary and an adjunct professor at the School of Computer Science at McGill University.
Ebrahimi Kahou’s and her group work on solving fundamental problems in representation learning for decision making, with a broad focus on generalization and efficient learning. Besides this primary focus, she also has expertise in knowledge distillation, climate modeling using deep learning, building large-scale datasets, clinical decision making, and NLP. Her works have been published in top-tier venues such as NeurIPS, ICLR, ICML, TMLR, CVPR, and ICCV.
Awards
- Ten-Year Technical Impact Runner-Up, 25th ACM International Conference on Multimodal Interaction, 2023
- Second place in the PROBA-V Super Resolution Challenge, European Space Agency, 2019
- Best Thesis Award in the Department of Computer Engineering, Polytechnique Montréal, 2017
- Leader of team that won the third place in the Emotion Recognition in the Wild Challenge, ICMI, 2015
- Best Paper Award, ECCV workshop on computer vision with local binary patterns, 2014
- Leader of team that won the first place in the Emotion Recognition in the Wild Challenge, 2013
Relevant Publications
- Agarwal, P., Andrews, S., & Ebrahimi Kahou, S. (2024). Learning to play Atari in a world of tokens. In Proceedings of the International Conference on Machine Learning.
- Joslin Kenfack, P., Ebrahimi Kahou, S., & Aïvodji, U. (2024). A survey on fairness without demographics. Transactions on Machine Learning Research.
- Sheth, I., & Ebrahimi Kahou, S. (2023). Auxiliary losses for learning generalizable concept-based models. In Advances in Neural Information Processing Systems.
- Sujit, S., Nath, S., Braga, P., & Ebrahimi Kahou, S. (2023). Prioritizing samples in reinforcement learning with reducible loss. In Advances in Neural Information Processing Systems.
- Goyal, R., Ebrahimi Kahou, S., Michalski, V., Materzynska, J., Westphal, S., Kim, H., Haenel, V., Fruend, I., Yianilos, P., Mueller-Freitag, M., et al. (2017). The "something something" video database for learning and evaluating visual common sense. In Proceedings of the International Conference on Computer Vision (Vol. 1, p. 3).