Invited Speakers

Marlene Kretschmer

Marlene studied mathematics before completing a PhD in climate physics at the Potsdam Institute for Climate Impact Research. She then worked as a postdoctoral researcher in the Department of Meteorology at the University of Reading (UK). Since 2022, she is a Junior Professor of Climate Causality at Leipzig University. Her research aims to understand the large-scale drivers of regional weather and climate, including extreme events, and how this knowledge can enhance predictions from subseasonal timescales to projections extending to the end of the century. She is particularly interested in applying causal inference and machine learning algorithms to identify key drivers and teleconnections in large climate model simulations and observational datasets. Her approach integrates data-driven methods with physical understanding to improve climate prediction and interpretation.

Sebastian Engelke

Sebastian is Full Professor at the Research Institute for Statistics and Information Science at the University of Geneva, where he is holding an Eccellenza grant. His research group works on: Extreme value theory and graphical models; extrapolation in machine learning; AI weather forecasting; and statistical climate science. Sebastian did his studies in Mathematics at University of Göttingen and UC Berkeley, and he obtained his PhD in 2013 at the University of Göttingen. He was then an Ambizione fellow at EPF Lausanne with Anthony Davison, and visiting professor at the Department of Statistical Sciences at the University of Toronto from 2018–2019.

David Rossell

David did his PhD at Rice University (Houston, USA) under Prof. Peter Müller, and a post-doc at MD Anderson Cancer Center (Houston, USA) under Prof. Valen Johnson. He then created a Biostatistics & Bioinformatics Unit at the Institute for Research in Biomedicine in Barcelona (Spain), which he headed for 5 years, after which he moved to the University of Warwick (Coventry, UK). He subsequently moved to Pompeu Fabra University, where he is currently based and where he directs the master in Data Science Methodology at the Barcelona School of Economics. David has worked in theoretical, methodological and applied statistics, for the latter mainly in Biomedicine, Social Sciences and Chemistry. His work focuses on high-dimensional inference, particularly model selection, structural learning and data integration, with an emphasis on Bayesian statistics. More specifically he has worked on non-local priors for model selection, posterior concentration theory, canonical mean and covariance models such as regression, GLMs, GAMs, graphical and factor models. He is co-editor at Bayesian Analysis, where he also served at Associate Editor for 9 years, he is serving as an AE at JASA for 6 years, and he previously served as AE at Computational Statistics and Data Analysis.

Pre-Conference Workshop Speakers

Urmi Ninad

Urmi is a theoretical physicist by training and currently a postdoctoral researcher at the University of Potsdam, where she focuses on advancing methods in causal inference. She is especially interested in spatiotemporal complex systems, with a particular emphasis on understanding causality in high-dimensional, multi-domain and non-stationary environments.