Dr. Peijun Song joined the Department of Statistics and Actuarial Science at the University of Waterloo in September 2018 as an Assistant Professor. Prior to this, he obtained his Ph.D. in Statistics at Simon Fraser University.
Peijun's current research interest is focused on functional data analysis, which concerns curves and function-valued variables. Brain imaging data such as EEG signals, function magnetic resonance imaging (FMRI) and di usion tensor imaging (DTI) are typical examples. He has a keen interest in applying functional data analysis techniques to study functional connectivity between these imaging data collected from different regions of the brain. Knowing these connectivities are of vital importance in practice, since it may provide new insights into some diseases that can be hardly diagnosed with traditional methods. In theoretical aspects, Peijun is concerned about large sample properties of high dimensional functional regression models proposed for these data. Also, dependence modelling with copula models for discrete and/or survival outcomes attracts my attention as well.