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). 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)