Irina Rish
Appointment
Canada CIFAR AI Chair
Pan-Canadian AI Strategy
About
Appointed Canada CIFAR AI Chair – 2019
Renewed Canada CIFAR AI Chair – 2024
Irina Rish is a Canada CIFAR AI Chair at Mila and a full professor at the Department of Computer Science and Operations Research (DIRO) at the University of Montreal. She holds the Canada Excellence Research Chair in Autonomous AI.
Rish’s extensive research career spans multiple AI domains, from automated reasoning and probabilistic inference in graphical models, to machine learning, sparse modeling, and neuroscience-inspired AI. Her current research endeavors concentrate on continual learning, out-of-distribution generalization, robustness; and understanding neural scaling laws and emergent behaviors (w.r.t. both capabilities and alignment) in foundation models – a vital stride towards achieving maximally beneficial Artificial General Intelligence (AGI).
Awards
- IBM Eminence & Excellence Award, 2018
- IBM Outstanding Innovation Award, 2018
- IBM Outstanding Technical Achievement Award, 2017
- IBM 10th Invention Plateau Award (63 Granted Patents), 2015
- Best Paper Award, ECML, 2003
Relevant Publications
- Khetarpal, K., Riemer, M., Rish, I., & Precup, D. (2022). Towards continual reinforcement learning: A review and perspectives. Journal of Artificial Intelligence Research 75, 1401-1476.
- Ahuja, K., Caballero, E., Zhang, D., Bengio, Y., Mitliagkas, I., & Rish, I. (2021). Invariance Principle Meets Information Bottleneck for Out-of-Distribution Generalization.
- Caccia, M., et al. (2020). Online fast adaptation and knowledge accumulation (osaka): a new approach to continual learning. Advances in Neural Information Processing Systems, 33, 16532 - 16545.
Bashivan, P., Rish, I., Yeasin, M., & Codella, N. (2015). Learning representations from EEG with deep recurrent-convolutional neural networks.
Rish, I. (2001). An empirical study of the naive Bayes classifier. In IJCAI 2001 workshop on empirical methods in artificial intelligence (Vol. 3, No. 22, pp. 41-46).