Thomas Schön


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

September 1, 2022 [New team members] LI am very glad to welcome two new members to the team, namely Ziwei Luo who just started as a PhD student. We also have a new pre-doc, Tim Martin who is visiting from Frank Allgöwer's group at the University of Stuttgart. Very much look forward to working with both of you!

August 30, 2022 [New paper accepted] Long time ago I was thinking about how variational inference can be used for estimating the states and the parameters in nonlinear state-space models. Now we have published a way of doing this for both states and parameters, the system identification solution was just accepted for Automatica and the state estimation solution was published in IEEE TSP some time ago, see this link (also on arXiv).

Jarrad Courts, Adrian Wills, Thomas B. Schön and Brett Ninness. Variational system identification for nonlinear state-space models. Automatica. (accepted) [arXiv]

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 © Thomas Schön 2022