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.
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.
Learning is essential for many real-world tasks, including recognition, diagnosis, forecasting and data-mining. This course covers a variety of learning scenarios (supervised, unsupervised and partially supervised), as well as foundational methods for regression, classification, dimensionality reduction and modeling. Techniques such as kernels, optimization and probabilistic graphical models will typically be introduced. It will also provide the formal foundations for understanding when learning is possible and practical. Prerequisites: CMPUT 204 or 275; MATH 125; CMPUT 267 or MATH 214; or consent of the instructor.
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 PSYCH 258. [Faculty of Science]