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I was recently a graduate student in the Psychology department at the University of Victoria, and will soon be a doctoral student at the Centre for Neuroscience Studies at Queens University. My research focuses on the motor system and its interactions with language and visual systems, which I study using a combination of computerized testing, motion tracking, and neuroimaging. Broadly, I’m interested in the ways in which task related information is used by the motor system when planning and executing movement.

My background is in statistics and mathematics, and a few of my side-projects involve or have involved using hierarchical Bayesian models to do meta-analysis, and using multi-scale multi-variate entropy measures to characterize EEG signal complexity in aging populations. A major interest of mine is the statistical analysis of three-dimensional rotation data. There are several methods of representing 3D rotations, and all of them involve significant trade-offs between ease of computation and ease of analysis. For example, quaternions provide a very elegant way of representing and computing with rotations, but their geometry makes it difficult to do statistical analysis. To overcome this, we would like to pass into another space (like \mathbb{R}^3) where statistics is easier, but the “shapes” of \mathbb{R}^3 and the space of 3D rotations are incompatible, and so passing between the two necessarily distorts the data in some way. Figuring out how to prepare the data in order to minimize this kind of distortion, and the best way to transform the data, involve some very deep mathematics.