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

June 18, 2019 [Paper accepted for IEEE Transactions on Signal Processing] I have been working on Sequential Monte Carlo (SMC) methods since I started my PhD back in December 2001 and this is definitely one of the most interesting developments I have been involved in (so far :-) ) when it comes to SMC. A key challenge is to extend the algorithm to high-dimensional spaces. In this work we take one step in this direction by developing a construction that generalises the SMC framework by requiring only approximate, properly weighted, samples from the SMC proposal distribution, while still resulting in a correct SMC algorithm! This opens the door to completely new (higher dimensional) application areas, such as for example nonlinear spatio-temporal state space models.

Christian A. Naesseth, Fredrik Lindsten and Thomas B. Schön. High-dimensional filtering using nested sequential Monte CarloIEEE Transactions on Signal Processing, 2019. (Accepted) [arXiv]

June 4, 2019 [Two papers accepted for L-CSS] In the first paper we propose an input design method for a general class of parametric probabilistic models (including nonlinear dynamical systems). By representing (samples from) the posterior as trajectories from a certain Hamiltonian system, we transform the input design task into an optimal control problem. In the second paper we study the problem of learning robust LQ-controllers, when the dynamics of the linear system are unknown. We develop a robust control synthesis method to minimize the worst-case LQ cost, with probability 1 − δ, given empirical observations of the system. The dual control strategy that we derive gives promising results in comparison with the common greedy random exploration strategies. We can do better than just random exploration.

Jack Umenberger and Thomas B. Schön. Nonlinear input design as optimal control of a Hamiltonian system. IEEE Control System Letters, 2019. [arXiv]

Mina Ferizbegovic, Jack Umenberger, Håkan Hjalmarsson and Thomas B. Schön. Learning robust LQ-controllers using application oriented exploration. IEEE Control System Letters, 2019.

May 15, 2019 [Paper accepted for UAI] This is our first paper within the area of phylogenetics and it also links in well in our series of papers making use of our new programming language Birch. More specifically we consider probabilistic programming for birth-death models of evolution and introduce a new widely-applicable inference method that combines an extension of the alive particle filter with automatic Rao-Blackwellization via delayed sampling. Birth-death models of evolution are an important family of phylogenetic models of the diversification processes that lead to evolutionary trees. Probabilistic programming languages give phylogeneticists a new and exciting tool: their models can be implemented as probabilistic programs with just a basic knowledge of programming.

Jan Kudlicka, Lawrence M. Murray, Fredrik Ronquist and Thomas B. Schön. Probabilistic programming for birth-death models of evolution using an alive particle filter with delayed sampling. In Conference on Uncertainty in Artificial Intelligence (UAI), Tel Aviv, Israel, July, 2019.

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 © Thomas Schön 2019