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Joachim M. Buhmann leads the group of “Pattern Recognition and Machine Learning” in the Department of Computer Science at ETH Zurich. He has been a full professor of Information Science and Engineering (Informatik) since October 2003.
Born in 1959 in Friedrichshafen, Germany, he studied Physics at the Technical University Munich and obtained his PhD in Theoretical Biophysics with Professor Klaus Schulten. His doctoral thesis was about pattern recognition in neural networks. He then spent three years as a research assistant and assistant professor at the University of Southern California, Los Angeles. He spent 1991 at the Lawrence Livermore National Laboratory in California. He held the chair of practical Computer Science (praktische Informatik) at the University of Bonn, Germany from 1992 to 2003.
His research interests spans the areas of pattern recognition and data analysis, including machine learning, statistical learning theory and applied statistics. Application areas of his research include image analysis, remote sensing and bioinformatics. He has been in the technical committee of the German Pattern Recognition Society (Deutschen Arbeitsgemeinschaft für Mustererkennung) since 1995, including serving on the board during 2000-2003. He is associate editor for IEEE Transactions on Neural Networks and IEEE Transactions on Image Processing.
Information Theoretic Model Validation by Approximate Optimization
as author at Discrete Optimization in Machine Learning,
Is non-(geo)metricity an issue for machine learning?
as author at 27th International Conference on Machine Learning (ICML), Haifa 2010,
together with: Shai Ben-David, Edwin Hancock, Alex Smola,
Information Theoretic Model Selection in Clustering
as author at Clustering,
Cluster stability and robust optimization
as author at Workshop on Stability and Resampling Methods for Clustering, Tübingen 2007,
Learning issues in image segmentation
as author at Workshop on Pattern Recognition and Machine Learning in Computer Vision, Grenoble 2004,