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

[LOOKING FOR POSTDOCS IN COMPUTATIONAL STATISTICS FOR MACHINE LEARNING] We have two postdoc positions open involving methodological development of new probabilistic models and new inference methods, which can for example include:

1. Developing efficient Bayesian inference algorithms for large-scale latent variable models in data rich scenarios.
2. Finding ways of systematically combining different inference techniques, such as variational inference, sequential Monte Carlo, and deep inference networks, resulting in new methodology that can reap the benefits of these different approaches.
3. Developing efficient black-box inference algorithms specifically targeted at inference in probabilistic programs. This line of research may include implementation of the new methods in the probabilistic programming language Birch, currently under development at the department.

Complete information on how to apply can be found here.

October 31, 2017 [New grant from the Swedish Research Council] My 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.

October 23, 2017 [Journal paper accepted for Mechanical Systems and Signal Processing] We are concerned with the problem of learning probabilistic nonlinear state space models of dynamical systems from measured data. In this invited tutorial we provide a self-contained introduction to one of the state-of-the-art methods - the particle Metropolis-Hastings algorithm - which has proven to offer a practical approximation. It is a Monte Carlo based method, where the particle filter is used to guide a Markov chain Monte Carlo method through the parameter space.

Thomas B. Schön, Andreas Svensson, Lawrence Murray and Fredrik Lindsten. Probabilistic learning of nonlinear dynamical systems using sequential Monte Carlo. Mechanical Systems and Signal Processing (MSSP), 2017. [pdf] [arXiv] [code]

September 25, 2017 [Journal paper accepted for IFAC Journal of Systems and Control] The Gaussian Process has three traditional shortcomings: 1) it is computationally intensive, 2) it cannot effectively deal with stochastic (noisy) inputs and 3) it cannot effectively incorporate new measurements in an online fashion. We have derived an algorithm that handles all three shortcomings. The new results are illustrated by solving nonlinear system identification problems.

Hildo Bijl, Thomas B. Schön, Jan-Willem van Wingerden and Michel Verhaegen. System identification through online sparse Gaussian process regression with input noise. IFAC Journal of Systems and Control, 2017. (Accepted for publication) [pdf] [arXiv] [ScienceDirect]

September 14, 2017 [Teaching the SMC methods course in Brussels] I will teach the SMC methods course in Brussels during the time period October 3 - 6, 2017. More complete information about the August edition of the course is available here.


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 © Thomas Schön 2017