More information about the vacancy: Teaching assistant Data
About the teaching
About the PhD project
This doctoral dissertation will be situated in the research domain of data-driven models for the study of extremes. The topic will deal with a data-driven study of extreme events and is situated in the field of extreme value statistics while exploring links with computational and machine learning methods. Assessing the probability of extreme events is of great importance in various life science applications given their potential for catastrophic impacts, e.g. in nature tsunamis, floods or heat waves can cause significant economic and human losses. While statistical models for univariate extremes are well-understood, classical models for multivariate extremes often lead to complex analyses that don't scale well when the number of variables increases and are hard to interpret or visualise. In this PhD track, we aim to build further on recent work in the field that allows more flexibility towards modelling the dependency structure in the tail of a distribution. A starting point for the research would be the study of sparse dependency structures that can be based on e.g. latent variable approaches or graphical models. Applications will be explored in the broad context of climate change issues among others.
Prof. dr. Stijn Luca