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

[NEWS: We have just opened a tenure track position in Automatic Control] We are looking for a new member to join our team. A short informal overview of some of our research is available here and a link to the advertisment is available here. The Swedish title of the position is “Biträdande universitetslektor”. Feel free to contact me if you have questions of any kind.

April 6, 2018 [Three conference accepted] We three papers accepted for the 18th IFAC Symposium on System Identification (SYSID) held in Stockholm this summer.

[C103] Carl Andersson, Niklas Wahlström and Thomas B. Schön. Data-driven impulse response regularization via deep learning. In Proceedings of the 18th IFAC Symposium on System Identification (SYSID), Stockholm, Sweden, July, 2018[arXiv]

[C102] Andreas Svensson, Dave Zachariah and Thomas B. Schön. How consistent is my model with the data? Information-theoretic model check. In Proceedings of the 18th IFAC Symposium on System Identification (SYSID), Stockholm, Sweden, July, 2018. [arXiv]

[C101] Andreas Svensson, Fredrik Lindsten and Thomas B. Schön. Learning nonlinear state-space models using smooth particle-filter-based likelihood approximations. In Proceedings of the 18th IFAC Symposium on System Identification (SYSID), Stockholm, Sweden, July, 2018. [arXiv]


March 21, 2018 [First pre-release of our new probabilistic programming language Birch] Within our research project ASSEMBLE (funded by the Swedish Foundation for Strategic Research) we are developing a new probabilistic programming language, which we call Birch. Everyone is welcome to play with it. More information is available here.

March 16, 2018 [Paper accepted for IEEE Transactions on Robotics] By using Maxwell's equations, we derive a probabilistic modeling approach for interpolation and extrapolation of the magnetic field. We model the magnetic field components jointly by imposing a Gaussian process (GP) prior on the latent scalar potential of the magnetic field. By rewriting the GP model in terms of a Hilbert space representation, we circumvent the computational pitfalls associated with GP modeling and provide a computationally efficient and physically justified modeling tool for the ambient magnetic field. The model allows for sequential updating of the estimate and time-dependent changes in the magnetic field and is applied in real-world applications. This is joint work with our friends Arno Solin and Simo Särkkä at Aalto University, Finland.

Arno Solin, Manon Kok, Niklas Wahlström, Thomas B. Schön and Simo Särkkä. Modeling and interpolation of the ambient magnetic field by Gaussian processes. IEEE Transactions on Robotics, 2018. (Accepted for publication) [arXiv] [video]

February 27, 2018 [Our team is now part of the WASP project] WASP stands for Wallenberg Artificial  Intelligence, Autonomous Systems and Software Program. It is Sweden’s largest ever individual research program. More information about the project is available here.

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 © Thomas Schön 2018