Bernhard Schölkopf



  • Advisory Committee Chair
  • Learning in Machines & Brains


  • Max Planck Institute for Intelligent Systems
Department of Empirical Inference


  • Germany


PhD (Computer Sciences), Technische Universität Berlin
Diplom (Physics), University of Tübingen
MSc (Mathematics), University of London


Bernhard Schölkopf is a computer scientist whose interests are in the fields of machine learning and inference from empirical data.

In particular, he studies kernel methods for extracting regularities from possibly high-dimensional data. These regularities are usually statistical ones, but in recent years he has also become interested in methods for finding causal structures that underlie statistical dependencies. He has worked on a number of different applications of machine learning, most recently for the work of astronomers and photographers.


Royal Society Milner Award

Academy Prize of the Berlin-Brandenburg Academy of Sciences and Humanities

Max Planck Research Award

J. K. Aggarwal Prize of the International Association for Pattern Recognition

Relevant Publications

Schölkopf, B. et al. "Computing Functions of Random Variables via Reproducing Kernel Hilbert Space Representations." Stat. Comput. 25, no. 4 (July 2015): 755–66.

Schölkopf, B. "Learning to see and act." Nature 518, no. 7540 (February 2015): 486–87.

Schölkopf, B. et al. "Removing systematic errors for exoplanet search via latent causes." In Proceedings of the 32nd International Conference on Machine Learning, vol. 37 (ICML 2015): 2218–2226.

Schölkopf, B., and A. Smola. Learning with Kernels. Cambridge, MA: MIT Press, 2002.