The topic deals 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 of the research is the study of sparse dependency structures that can be based on e.g. latent variable approaches or graphical models. Applications are being explored in the broad context of climate change issues among others.