Bernhard Scholkopf

ACM AAAI Allen Newell Award

Germany - 2022

citation

In recognition of his widely used research in machine learning, advancing both mathematical foundations and a broad range of applications in science and industry

Professor Bernhard Schölkopf is a world leader in machine learning, and has made seminal contributions to kernel methods, learning theory, and causality. Professor Schölkopf helped develop, advance, and generalize the theory of support vector machines, which has had a profound impact on machine learning. His contributions to kernel PCA and kernel embeddings have advanced fundamental statistical methodology in dimensionality reduction, semi-supervised learning, and hypothesis testing. Besides his theoretical and algorithmic contributions, Professor Schölkopf and his team have advanced numerous areas of applied machine learning, including applications to astronomy, biology, computer vision, robotics, neuroscience, and cognitive science. Professor Schölkopf?s pioneering work in causal machine learning has laid the foundation for a novel understanding of learning causal relationships from data, with implications for all areas of science. 

Apart from his fundamental contributions to science, Professor Schölkopf has played a critical leadership role in the global and especially European AI and machine learning community. He founded the Max Planck Institute for Intelligent Systems, and the European Laboratory for Learning and Intelligent Systems (ELLIS), two power-houses of ML research. He co-founded the Machine Learning Summer Schools, which have educated many generations of ML researchers, and the Cyber Valley research consortium fueling entrepreneurial advances. He has served as EIC for JMLR and is among the world?s most cited computer scientists. His work has been honored with the Royal Society Milner Award and the BBVA Foundation Frontiers of Knowledge Award.

Press Release

2022 ACM - AAAI Allen Newell Award

Bernhard Schölkopf, Max Planck Institute for Intelligent Systems and ETH Zurich, and Stuart J. Russell, University of California at Berkeley, receive the ACM - AAAI Allen Newell Award.

Schölkopf is recognized for his widely used research in machine learning, advancing both mathematical foundations and a broad range of applications in science and industry.

Schölkopf has made fundamental contributions to kernel methods and causality. His contributions to kernel PCA and kernel embeddings have advanced fundamental statistical methodology in dimensionality reduction and hypothesis testing. Professor Schölkopf and his team have advanced numerous areas of applied machine learning, including applications to astronomy, biology, computer vision, robotics, neuroscience, and cognitive science. Schölkopf’s pioneering work in causal machine learning has laid the foundation for a novel understanding of learning causal relationships from data, with implications for all areas of science.

Russell is recognized for a series of foundational contributions to Artificial Intelligence, spanning a wide range of areas such as logical and probabilistic reasoning, knowledge representation, machine learning, reinforcement learning, and the ethics of AI.

Early in his career, Russell defined and studied the concept of bounded optimality, for which he received the 1995 IJCAI Computers and Thought Award. His book, Artificial Intelligence: A Modern Approach (co-authored with Peter Norvig), is the preeminent textbook for AI. It has been used for decades to train AI students in more than 1,500 universities all over the world. Russell’s work on BLOG (Bayesian Logic) led to the creation of the NETVISA global seismic monitoring algorithm that has the capability to reliably detect and accurately localize nuclear explosions. In recent years he has also become an influential figure in addressing ethical issues in AI.