Nicolai Meinshausen

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About

Nicolai Meinshausen is a leading figure in statistical machine learning, with foundational contributions to high-dimensional inference, causal discovery, and robust statistics. His work on the Lasso and stability selection has provided rigorous methods for variable selection in high-dimensional settings, where the number of predictors can vastly exceed observations. Meinshausen co-developed the "randomized Lasso" and the "stability selection" framework, which have become standard tools for controlling false discoveries in genomics, neuroscience, and climate science. His highly cited papers, including those on the "Lasso for linear models" and "Stability selection," have accumulated tens of thousands of citations, reflecting their profound impact on both theory and practice. He has also advanced causal inference through the "PC algorithm" and "Invariant Causal Prediction," enabling robust discovery of cause-effect relationships from observational data. Beyond methodology, Meinshausen has applied these techniques to climate modeling, notably in detecting and attributing changes in extreme weather events. His work bridges rigorous theory with accessible software, making complex statistical tools widely usable. A professor at ETH Zurich, his research continues to shape how scientists extract reliable insights from complex, high-dimensional datasets.