natural language processing neuroscience machine learning
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.
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.
Models of language meaning (semantics) are typically built using large bodies of text (corpora) collected from the Internet. These corpora often contain billions of words, and thus cover the majority of the ways words are used. However, to build computer programs that truly understand language, and can understand more rare and nuanced word usage, we need algorithms that can generalize beyond common word usage. By collecting brain images of people reading, we can explore how the human brain handles the complexities of language, which could inspire the next generation of semantic models.
I teach graduate course 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 CS (466/566) and psych 354.
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: STAT 141 or 151 or 161 or SCI 151 and PSYCO 258. [Faculty of Science]Winter Term 2022