Matthias W. Seeger recently joined the Machine Learning Group at Amazon Berlin. Previously, he was an assistant professor at the School of Computer and Communication Sciences at the Ecole Polytechnique Federale de Lausanne. Dr. Seeger has made seminal contributions to theory and practice of Gaussian process models, PAC-Bayesian learning theory, and variational approximations of Bayesian inference for sparse generalized linear models. His work applies to problems in computer vision and imaging, compressive sensing, and bioinformatics. E.g., he has shown that magnetic resonance imaging sequences can be optimized by means of Bayesian experimental design. His talk in the BioImage Geek Seminar Series will be a tutorial on variational Bayesian inference and experimental design.
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