Alona Fyshe
Appointment
Fellow
Canada CIFAR AI Chair
CIFAR Azrieli Global Scholar 2016-2018
Learning in Machines & Brains
Pan-Canadian AI Strategy
About
Appointed Canada CIFAR AI Chair – 2018
Renewed Canada CIFAR AI Chair – 2024
Alona Fyshe is a Fellow in CIFAR’s Learning in Machines & Brains program, a CIFAR Azrieli Global Scholar 2016-2018, and Canada CIFAR AI Chair at Amii. She is an assistant professor at the University of Alberta.
Alona Fyshe is a Fellow in CIFAR’s Learning in Machines & Brains program, a CIFAR Azrieli Global Scholar 2016-2018, and Canada CIFAR AI Chair at Amii. She is an associate professor at the University of Alberta.
Fyshe uses machine learning to analyze brain images collected while people read text or view images, which allows her to study how the human brain represents meaning. She also studies how computer models learn to represent meaning when trained on text or images. She leverages the connections between computer representations of meaning and those found in the human brain in order to advance our understanding of the brain, and the state of the art in machine learning.
Awards
- AI in Research - AI Researcher of the Year (First Runner Up), Women in AI, 2022
- Top 40 under 40, Edify, 2020
Relevant Publications
- Ghanem, B., Lutz Coleman, L., Rivard Dexter, J., McIntosh von der Ohe, S., & Fyshe, A. (2022). Question Generation for Reading Comprehension Assessment by Modeling How and What to Ask. In Findings of the Association for Computational Linguistics, (pp. 2131–2146).
- Banman, K., Peet-Pare, G. L., Hegde, N., Fyshe, A., & White, M. (2022). Resonance in Weight Space: Covariate Shift Can Drive Divergence of SGD with Momentum In The 10th International Conference on Learning Representations.
- Hashemzadeh, M., Kaufeld, G., White, M., Martin, A. E., & Fyshe, A. (2020). From Language to Language-ish: How Brain-Like is an LSTM's Representation of Nonsensical Language Stimuli? In Findings of the Empirical Methods in Natural Language Processing.
- Haoyan, X., Murphy, B., & Fyshe, A. (2016). BrainBench: A Brain-Image Test Suite for Distributional Semantic Models. SIGDAT Conference on Empirical Methods for Natural Language Processing, Austin, Texas.
- Fyshe, A., Talukdar, P.P, Murphy, B., & Mitchell, T.M. (2014). Interpretable Semantic Vectors from a Joint Model of Brain- and Text-Based Meaning. Annual meeting of the Association for Computational Linguistics, Baltimore, Maryland.