Professor Piotr Fryzlewicz

About

Piotr Fryzlewicz's research interests lie in multiscale modelling and estimation, time series (especially nonstationary time series), change-point detection, high-dimensional statistical inference and dimension reduction, statistical learning, networks, functional programming in data science, statistics in finance, statistics in the social sciences, and statistics in neuroscience. Amongst others, Piotr is the originator of the Haar-Fisz transformation and smoothing methodology for non-Gaussian data and the Wild Binary Segmentation method for multiple change-point detection, as well as having co-authored work on Sparsified Binary Segmentation for high-dimensional time series segmentation and tilted correlation for variable selection in high-dimensional regression models.