Sebastian Engelke

Sebastian Engelke is an Associate Professor at the Research Center for Statistics at the University of Geneva. His research focuses on extreme value theory, spatial statistics, graphical models, and machine learning applications in climate science and risk assessment. He holds an SNSF Eccellenza Grant and has contributed to foundational work on extremal graphical models and causal inference in heavy-tailed systems.

• Affiliations: University of Geneva, Research Center for Statistics
• Grants: SNSF Eccellenza Grant (2020–2025), Ambizione Fellowship (2015–2018)

Education: PhD in Mathematics (2013, Georg-August-University of Göttingen), studies at UC Berkeley and University of Göttingen.

Research Interests

Engelke’s work bridges extreme value theory with modern machine learning, addressing challenges in climate extremes, risk quantification, and graphical model structures. Key areas include:

• Statistical modeling of multivariate extremes
• Causal discovery in heavy-tailed distributions
• Applications in environmental science and climate informatics

Key Contributions

Recent work includes advancements in extremal graphical models (via matrix completions), neural network approaches for extreme quantile regression, and validation methods for deep learning weather models. His research is published in top journals like Annals of Statistics and Journal of the American Statistical Association.

Awards & Recognition

• Lambert Award (2021)
• Gumbel Lecture (2023)
• Fields Research Fellowship (2019)

Teaching & Supervision

Engelke teaches courses on machine learning, probability, and advanced topics in statistics at the University of Geneva. He supervises PhD students and postdocs in machine learning and extreme value theory. His students include Edoardo Vignotto (extremal random forests) and Nicola Gnecco (causal discovery in heavy-tailed models).