Matthias Seeger

Matthias Seeger is a Principal Applied Scientist at Amazon. After his PhD (2003 at Edinburgh University), he worked at the University of California in Berkeley until 2005, and at the Max Planck Institute for Biological Cybernetics in Tübingen until 2008. He headed an independent research group within the DFG Cluster of Excellence on Multimodal Computing and Interaction at Saarland University and the MPI for Informatics in Saarbrücken until 2010. He was then an assistant professor at the School of Computer and Communication Sciences (EPFL), and from 2014 to 2016 a Senior Machine Learning Scientist at Amazon.

Research Interests
Matthias Seeger is interested in the theory, algorithmics, and practice of Bayesian techniques and probabilistic machine learning, with applications to computer vision and imaging, (medical) image processing, compressive sensing, bioinformatics, modelling of neural recordings, and advanced large-scale data analysis. His current research focusses on low-level image processing and sampling optimization (adaptive compressive sensing), with applications to magnetic resonance imaging.

Matthias Seeger made seminal contributions to the theory, algorithmics, and implementation of nonparametric Gaussian process models, semisupervised learning, PAC-Bayesian learning theory, and variational approximations of Bayesian inference for sparse generalized linear models. He was an invited fellow at the Statistical Theory and Methods for Complex, High-Dimensional Data programme, Isaac Newton Institute, Cambridge, UK. In 2012 Matthias Seeger received the ERC Starting Grant from the European Research Council.