Thomas Schön

Thomas Schön, Professor of Automatic Control at Uppsala University. Photo: Mikael Wallerstedt

Our aim is to automate the extraction of knowledge and understanding from data. Allowing machines (and humans) to understand what is happening and to acquire new skills and learn new things. We achieve this by developing new probabilistic models and deriving algorithms capable of learnings these models from data. The systematic use of probability in representing and manipulating these models is key. It allows us to represent not only what we know, but to some extent also what we do not know. We take a particular interest in dynamical phenomena evolving over time.

Our research is multi-disciplinary and it sits somewhere on the intersection of the areas of Machine learning and statistics, signal processing, automatic control and computer vision. We pursue both basic and applied research, which explains our tight collaboration with various companies. A slightly more detailed overview of our research is available here.

Recent research results/news

January 16, 2017 [NewLEADS in the news] A popular scientific article about our new research environment NewLEADS is available here.

December 22, 2017 [Paper accepted for AISTATS] This is our first paper making use of our new probabilistic programming language (Birch). The contribution is a construction that automatically makes use of Rao-Blackwellization to exploit a conditionally linear Gaussian sub-structure when it is present in the model.

[C99] Lawrence M. Murray, Daniel Lundén, Jan Kudlicka, David Broman and Thomas B. Schön. Delayed sampling and automatic Rao-Blackwellization of probabilistic programs. In Proceedings of the 21st International Conference on Artificial Intelligence and Statistics (AISTATS)Lanzarote, Spain, April, 2018. [arXiv]

October 31, 2017 [New grant from the Swedish Research Council] Our new ideas on how to build flexible models for nonlinear dynamical systems were granted from the Swedish Research Council. More details about the call are available here.

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 © Thomas Schön 2018