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Bernhard Schölkopf is distinguished for scientific contributions to Machine Learning. In 2002, he founded the Department for Empirical Inference at the Max Planck Institute for Biological Cybernetics, and led it to become an internationally renowned center for Machine Learning. In 2011, he became a founding director at the Max Planck Institute for Intelligent Systems with sites in Stuttgart and Tübingen.
He pointed out how positive definite kernels allow us to generalize arbitrary inner product algorithms to nonlinear settings as well as to non-vectorial data types, leading to the foundation of the field of kernel methods in machine learning. He made significant contributions in application domains from Biology and Medicine to Astronomy, thus helping to establish Machine Learning as a method to detect patterns in observational data in a range of scientific disciplines. More recently, he has been studying causal inference on the basis of empirical data, developing and assaying methods to identify cause and effect.