My research focuses on the joint analysis of imaging and genetics data to provide useful insight into phenotypic and genetic characteristics of epilepsy. By using classical multivariate techniques such as Partial Least Squares (PLS), I find associations between imaging and genetics features to help drive a better understanding of the relationships between brain features and genetic markers in epilepsy patients.
PLS finds pairs of vectors with maximum covariation between two datasets. By exploring these pairs of vectors, we can find linear associations between the datasets and identify which features contribute the most to the associative effect.
My research also involves exploring the application of non-linear and deep learning multi-view methods to epilepsy data. These methods have many benefits compared to their classical multivariate counterparts, such as finding non-linear associations and the potential to generate missing data.