Alona Fyshe, PhD, MSc, BSc

Area of Study / Keywords

natural language processing neuroscience machine learning

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About

I hold a Canadian Institute for Advanced Research (CIFAR) Artificial Intelligence Chair. I am a fellow of the “Learning in Machines and Brains” CIFAR Program, and a fellow of the Alberta Machine Intelligence Institute (amii). My BSc and MSc are from the University of Alberta, and my PhD is from Carnegie Mellon University.

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Research

My interests are Computational Linguistics, Machine Learning and Neuroscience. My work combines all three of these areas to study the way the human brain processes language.

I use machine learning to analyze brain images collected while people read text or view images, which allows my lab to study how the human brain represents meaning.  We also study how computer models learn to represent meaning when trained on text or images.  We leverage 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.

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Teaching

I teach graduate courses on machine learning and neuroscience in both the computing science and psychology departments. In the past I have also taught upper level machine learning in computing science (466/566), psych 354 and the AI Everywhere classes (INTD 161 and 461).

CMPUT 399 - Topics in Computing Science

This topics course is designed for a one on one individual study course between a student and an instructor. Prerequisites are determined by the instructor in the course outline. See Note (3) above.

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INT D 461 - Artificial Intelligence Everywhere Capstone

Students from different fields, with diverse backgrounds, will have a hands-on opportunity to work in teams to apply artificial intelligence (AI) or machine learning (ML) to solve challenging problems from the community. Students will apply best practices in teamwork and communication, and reinforce how to address issues such as bias and fairness within the developed solution or analysis. Students will share interdisciplinary insights into how AI and ML can be applied across different disciplines. Prerequisites: INT D 161, and one of CMPUT 200, 300, NS 115, PHIL 250, 366, or 385. Credit cannot be obtained in both CMPUT 469 and INT D 461.

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PSYCH 354 - Foundations of Cognitive Science

An introduction to the theories and research practices of cognitive science by examining contributions of cognitive psychology, artificial intelligence, linguistics, and neuroscience to a variety of research areas. Prerequisites: PSYCH 213 or STAT 151 or 161, and PSYCH 258. [Faculty of Science]

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Browse more courses taught by Alona Fyshe

arXiv. 2022 January;

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Dhanush Dharmaretnam, Chris Foster, Alona Fyshe

Neural Networks. 2021 May; 10.1016/j.neunet.2020.12.009

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Maryam Honari-Jahromi, Brea Chouinard, Esti Blanco-Elorrieta, Liina Pylkkänen, Alona Fyshe

PLOS ONE. 2021 March; 10.1371/journal.pone.0242754

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Findings of the Association for Computational Linguistics Findings of ACL: EMNLP 2020. 2020 January;

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Alona Fyshe, Gustavo Sudre, Leila Wehbe, Nicole Rafidi, Tom M. Mitchell

Human Brain Mapping. 2019 October; 10.1002/hbm.24714

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Alona Fyshe, Leila Wehbe, Partha Talukdar, Brian Murphy, and Tom Mitchell

North American Chapter of the Association for Computational Linguistics - Human Language Technologies (NAACL HLT).

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Haoyan Xu, Brian Murphy, Alona Fyshe

Empirical Methods for Natural Language Processing (EMNLP).

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Brian Murphy, Leila Wehbe, Alona Fyshe

Language, Cognition, and Computational Models, Cambridge University Press.

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Dhanush Dharmaretnam, Alona Fyshe

The North American Chapter of the Association for Computational Linguistics - Human Language Technologies (NAACL HLT 2018).

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