About
Peter Bühlmann is a Professor at ETH Zürich within the Seminar für Statistik, focusing on high-dimensional statistics, causal inference, and machine learning. His work bridges theoretical advancements with practical software implementations in R packages like pcalg, mboost, and glmmlasso, impacting fields such as genomics, proteomics, and intensive care analytics.
- Key Contributions: Causal structure learning, stability selection, anchor regression, and deconfounding.
- Software: Developed widely used R packages for statistical modeling and causal inference.
- Teaching: Courses on high-dimensional statistics at ETH Zürich and international institutions.
Research Trends: Recent articles emphasize causal robustness, domain adaptation, and applications in medicine. His work addresses challenges in heterogeneous data, missing values, and covariate shifts using methods like spectral deconfounding and residual prediction tests.
Scientific Recognition: Co-author of a paper designated as a New Hot Paper (Meinshausen and Bühlmann, 2006) by Essential Science Indicators, indicating significant impact in high-dimensional multiple testing.
