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

July 4, 2018 [Paper accepted for JASA] We have introduced a new coupling construction for particle filters which we also exploit to derive a new unbiased estimator of smoothing expectations. The lack-of-bias property has methodological benets in that independent estimators can be generated in parallel and condence intervals can be constructed from the central limit theorem. To design unbiased estimators, we combine a generic debiasing technique for Markov chains and a Markov chain Monte Carlo algorithm for smoothing.

Pierre E. Jacob, Fredrik Lindsten and Thomas B. Schön. Smoothing with couplings of conditional particle filters. Journal of the American Statistical Society (JASA), 2018. [arXiv]

June 7, 2018 [New Automatica paper] We revisit the classical problem of maximum likelihood learning of linear time-invariant state space models. Our contribution is that we add model stability guarantees by combining Lagrangian relaxation with the expectation maximization (EM) algorithm to build tight bounds on the likelihood. We then derive two algorithms that can be used to optimize these bounds over convex parameterizations of all stable linear models using semidefinite programming.

Jack Umenberger, Johan Wågberg, Ian Manchester and Thomas B. Schön. Maximum likelihood identification of stable linear dynamical systems. Automatica, 2018. (Accepted for publication) [arXiv] [code]

June 1, 2018 [2 guest PhD students] Over the coming month I am very glad to welcome our two guest PhD students Timothy Rogers from the University of Sheffield in the UK and Matteo Scandella from the University of Bergamo in Italy. Both will be working on sequential Monte Carlo and Birch.

May 11, 2018 [Paper accepted for ICML] We provide new results for the problem of predicting spatio-temporal processes with temporal patterns that vary across spatial regions, when data is obtained as a stream. More specifically, we develop a localized spatio-temporal covariance model of the process that can capture spatially varying temporal periodicities in the data. We then apply a covariance-fitting methodology to learn the model parameters which yields a predictor that can be updated sequentially with each new data point.

[C105] Muhammad Osama, Dave Zachariah and Thomas B. Schön. Learning localized spatio-temporal models from streaming data. In Proceedings of the 35th International Conference on Machine Learning (ICML)Stockholm, Sweden, July, 2018. [arXiv]

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 © Thomas Schön 2018