Samira Ebrahimi Kahou
About
Samira Ebrahimi Kahou is a Canada CIFAR AI Chair at Mila, an associate professor at École de technologie supérieure (ÉTS), and an adjunct professor at the School of Computer Science at McGill University.
Ebrahimi Kahou’s current research interests are multimodal learning, reasoning across modalities, metalearning and in general efficient representation learning. She also works on deep learning methods for dialogue systems and humanitarian AI.
Awards
- Member of team that won the 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
Kim, S., Kim, H., Lee, J., Yoon, S., Kahou, S. E., Kashinath, K., & Prabhat, M. (2019). Deep-hurricane-tracker: Tracking and forecasting extreme climate events. In 2019 IEEE Winter Conference on Applications of Computer Vision (WACV) (pp. 1761-1769). IEEE.
Goyal, R., Ebrahimi Kahou, S., Michalski, V., Materzynska, J., Westphal, S., Kim, H., … & Memisevic, R. (2017). The” something something” video database for learning and evaluating visual common sense. In Proceedings of the IEEE international conference on computer vision (pp. 5842-5850).
The Theano Development Team., Al-Rfou, R., Alain, G., Almahairi, A., Angermueller, C., Bahdanau, D., … & van Tulder, G. (2016). Theano: A Python framework for fast computation of mathematical expressions.Kahou, S. E.,
Bouthillier, X., Lamblin, P., Gulcehre, C., Michalski, V., Konda, K., … & Bengio, Y. (2016). Emonets: Multimodal deep learning approaches for emotion recognition in video. Journal on Multimodal User Interfaces, 10(2), 99-111.
Kahou, S. E., Pal, C., Bouthillier, X., Froumenty, P., Gülçehre, Ç., Memisevic, R., … & Wu, Z. (2013). Combining modality specific deep neural networks for emotion recognition in video. In Proceedings of the 15th ACM on International conference on multimodal interaction (pp. 543-550).
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CIFAR is a registered charitable organization supported by the governments of Canada, Alberta and Quebec, as well as foundations, individuals, corporations and Canadian and international partner organizations.