Researcher

Bernhard Schölkopf

Also known as Bernhard Schoelkopf Bernhard Scholkopf

Bernhard Schölkopf helped develop kernel methods, which let learning algorithms work with structured comparisons between inputs. He studied mathematics, physics and computer science, then worked across research institutes and companies before becoming a Max Planck director in 2001. With Thomas Hofmann and Alex Smola, he wrote a broad review of kernel learning. His later research connects machine learning with causal inference, including the problem of finding causal variables in observed data. In 2020, he shared a Frontiers of Knowledge award with Vladimir Vapnik and Isabelle Guyon. He held a leadership role at the ELLIS Institute Tübingen in 2023. He is an ELLIS unit and program director and fellow, with Tübingen and Max Planck affiliations.

• Kernel methods
• Causal learning
• Learning theory