Keynote Speaker: Klaus-Robert MÜLLER

August, 28, 2018, 11:00 – 12:00, Tuesday

Prof. Dr. Klaus-Robert MÜLLER
Machine Learning Group TU Berlin, MPI for Informatics, Saarbrücken, and Korea University, Seoul

Title: Machine Learning and AI for the Sciences —Towards Understanding

Abstract: In recent years, machine learning (ML) and artificial intelligence (AI) methods have begun to play a more and more enabling role in the sciences and in industry. In particular, the advent of large and/or complex data corpora has given rise to new technological challenges and possibilities. In his talk, Müller will touch upon the topic of ML applications in the sciences, in particular in neuroscience, medicine and physics. He will also discuss possibilities for extracting information from machine learning models to further our understanding by explaining nonlinear ML models. E.g. Machine Learning Models for Quantum Chemistry can, by applying interpretable ML, contribute to furthering chemical understanding. Finally, Müller will briefly outline perspectives and limitations.

Bio: Klaus-Robert Müller studied physics (Master-1989) and computer science (PhD-1992) in Karlsruhe, did a Postdoc at GMD FIRST (1992-1994) and at the University of Tokyo (1994/95), then founded the Intelligent Data Analysis group at GMD FIRST (1995) and became Professor at the University of Potsdam (1999). Since 2006 he is Machine Learning Professor at TU Berlin; directing the Bernstein Center for Neurotechnology Berlin (-2014) and from 2014 co-directing the Berlin Big Data Center. He was awarded the Olympus Prize for Pattern Recognition (1999), the SEL Alcatel Communication Award (2006), the Science Prize of Berlin by the Governing Mayor of Berlin (2014), the Vodafone Innovations Award (2017). In 2012, he was elected member of the German National Academy of Sciences-Leopoldina, in 2017 of the Berlin Brandenburg Academy of Sciences and also in 2017 external scientific member of the Max Planck Society. His research interests are intelligent data analysis and Machine Learning in the sciences (Neuroscience, Physics, Chemistry).