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Bernhard Schölkopf


  • Fellow
  • Learning in Machines & Brains




Bernhard Schölkopf’s scientific interests are in machine learning and causal inference.

He has applied his methods to a number of different fields, ranging from biomedical problems to computational photography and astronomy. Bernhard has researched at AT&T Bell Labs, at GMD FIRST, Berlin, and at Microsoft Research Cambridge, UK, before becoming a Max Planck director in 2001. He is a member of the German Academy of Sciences (Leopoldina) and a Fellow of the ACM.

Bernhard co-founded the series of Machine Learning Summer Schools, and currently acts as co-editor-in-chief for the Journal of Machine Learning Research, an early development in open access and today the field’s flagship journal.

Photo: David Ausserhofer


  • Leibniz Prize, 2018
  • Royal Society Milner Award, 2014
  • Academy Prize of the Berlin-Brandenburg Academy of Sciences and Humanities, 2012
  • J. K. Aggarwal Prize of the International Association for Pattern Recognition, 2006
  • Annual PhD Award of the German Computer Science Association, 1998

Relevant Publications

  • J. Peters, D. Janzing, and B. Schölkopf. Elements of Causal Inference - Foundations and Learning Algorithms. MIT Press, Cambridge, MA, USA, 2017
  • B. Schölkopf, D. Hogg, D. Wang, D. Foreman-Mackey, D. Janzing, C.-J. Simon-Gabriel, and J. Peters. Modeling confounding by half-sibling regression. Proceedings of the National Academy of Science (PNAS), 13(27):7391–7398, 2016
  • B. Schölkopf, D. Janzing, J. Peters, E. Sgouritsa, K. Zhang, and J. Mooij. On causal and anticausal learning. In J. Langford and J. Pineau, editors, Proceedings of the 29th International Conference on Machine Learning (ICML), pages 1255–1262, New York, NY, USA, 2012. Omnipress
  • D. Janzing and B. Schölkopf. Causal inference using the algorithmic Markov condition. IEEE Transactions on Information Theory, 56(10):5168–5194, 2010
  • B. Schölkopf and A. J. Smola. Learning with Kernels. MIT Press, Cambridge, MA, USA, 2002

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