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Plenary speaker

Peter Bühlmann

ETH Zürich

Personal webpage

Biosketch

Peter Bühlmann is Professor of Mathematics and Statistics, and Director of Foundations of Data Science at ETH Zürich. He studied mathematics at ETH Zürich and received his doctoral degree in 1993 from the same institution. He was then a Postdoctoral Fellow from 1994-1995 and a Neyman Assistant Professor from 1995 - 1997 at UC Berkeley. From 2013 - 2017, he has been Chair of the Department of Mathematics at ETH Zürich. He is a Fellow of the Institute of Mathematical Statistics, a Fellow of the American Statistical Association, and he has been Co-Editor of the Annals of Statistics from 2010 - 2012. Other honors which he recently received include Doctor Honoris Causa from the Université Catholique de Louvain in 2017, Neyman Lecturer 2018 elected by the Institute of Mathematical Statistics, Rothschild Lecture 2018 at the Newton Institute (Cambridge), Guy Medal in Silver 2018 from the Royal Statistical Society, and he has been an invited speaker to the ICM 2018 in Rio de Janeiro and to the ICIAM 2019 in Valencia.

 

Research area

Peter Bühlmann's main research field is statistics. His interests are in high-dimensional and computational statistics, machine learning, causal inference and interdisciplinary applications in biology and medicine. Together with Sara van de Geer, he has co-authored the well-known book "Statistics for High-Dimensional Data: Methods, Theory and Applications", published in 2011, which covers a large array of the authors' research on the mathematical foundations and methodological principles for high-dimensional statistical inference. He has also been interested in fundamental connections between causality and probabilistic invariances and robustness. Joint work with Nicolai Meinshausen and Jonas Peters, read before the Royal Statistical Society in 2016, has enabled novel approaches for gaining stability and replicability of statistical studies, methods and computational algorithms; and vice versa, stability sheds new light on causal inference. Both lead to much desired improved interpretation of algorithms. This rapidly growing area at the intersection of statistics, machine learning and data science occupies a major part of Peter Bühlmann's current research.