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CANCELLED: Special Clean Energy Institute/DIRECT Seminar: Klaus-Robert Müller, Technische Universität Berlin
WhenFriday, Dec 2, 2016, 3:30 – 4:30 p.m.
Campus locationGowen Hall (GWN)
Campus roomGowen 301
Event typesLectures/Seminars

Machine Learning meets Quantum Chemistry

The talk will first briefly introduce machine learning (ML) concepts, before applying them in Quantum chemistry and materials.  This will include kernel-based learning methods and deep neural networks.  A particular focus will lie on the challenge of interpreting nonlinear machine learning models.  In other words, given that we have an excellent predictor of quantum chemical properties, how can we gain an understanding of the physics or chemistry that this learning machine has implemented?  I will show selected examples of ML applied for predicting properties of small molecules and also for materials.

Klaus-Robert Müller has been a professor of computer science at Technische Universität Berlin since 2006; at the same time, he has been the director of the Bernstein Focus on Neurotechnology Berlin until 2012 and is co-directing the Berlin Big Data Center from 2012.  He studied physics in Karlsruhe from 1984 to 1989 and obtained his Ph.D. degree in computer science at Technische Universität Karlsruhe in 1992.  After completing a postdoctoral position at GMD FIRST in Berlin, he was a research fellow at the University of Tokyo from 1994 to 1995.  In 1995, he founded the Intelligent Data Analysis group at GMD-FIRST (later Fraunhofer FIRST) and directed it until 2008.  From 1999 to 2006, he was a professor at the University of Potsdam.  He was awarded the 1999 Olympus Prize by the German Pattern Recognition Society, DAGM, and, in 2006, he received the SEL Alcatel Communication Award, and, in 2014 he was granted the Science Prize of Berlin awarded by the Governing Mayor of Berlin.  In 2012, he was elected to be a member of the German National Academy of Sciences-Leopoldina.  His research interests are intelligent data analysis, machine learning, signal processing, with applications in the neurosciences e.g.brain-computer interfaces, physics and genetics in addition to broad industrial applied research in machine learning.

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