Research and Teaching Interests
My main research interests are dimension reduction methods for high-dimensional data. This includes linear approaches (e.g. PCA, CCA, PCEV, PLS) as well as nonlinear approaches (e.g. manifold learning, autoencoders). High-dimensional data is challenging to analyse because of the so-called “curse of dimensionality”. However, we can mitigate this curse by using the structure in the data to our advantage.
I am interested in developing statistical methodologies that are statistically and computationally efficient. I am also interested in applications to statistical genetics, genomics, and neuroimaging.
- Fall 2019: STAT 4690—Applied Multivariate Analysis (course website)
- Winter 2020: STAT 7200—Multivariate Statistics (course website)
- Fall 2020: STAT 3150—Statistical Computing
- Winter 2021: SCI 2000—Introduction to Data Science (course website)