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

September 12, 2018 [New team members!] I am very glad to welcome two new PhD students to our team, Fredrik Gustafsson and Niklas Gunnarsson (WASP industrial PhD student together with Elekta). We also have a visiting PhD student Antônio Ribeiro from Universidade Federal de Minas Gerais (UFMG) in Belo Horizonte, Brazil. Antônio will visit us for one year and we will be working on the intersection of machine learning and control, especially with connections to optimization.

September 5, 2018 [Paper accepted for NIPS] This work deals with decision making for dynamical systems in the presence of uncertainty. More specifically, the problem we address lies at the intersection of reinforcement learning and robust control, and can be summarized as follows: given observations from an unknown dynamical system, we seek a policy to optimize the expected cost (as in reinforcement learning), subject to certain robust stability guarantees (as in robust control).

We have for a long time been developing algorithms to learn probabilistic models of dynamical systems from measured data. This is the first work where we are trying to make use of these models to build controllers with stability guarantees. The recent series of blog posts (nicely summarized in this paper) of Ben Recht was a great source of inspiration for our work.

[C106] Jack Umenberger and Thomas B. Schön. Learning convex bounds for linear quadratic control policy synthesis. In Neural Information Processing Systems (NIPS), Montréal, Canada, December 2018. [arXiv]

September 3, 2018 [New PhD thesis to be defended] On October 12 at 10.15 Andreas Svensson will present and defend his PhD thesis in room ITC/2446 here at Uppsala University. The title of the thesis is: Machine learning with state-space models, Gaussian processes and Monte Carlo methods. The thesis is available here. The opponent is Professor Carl Rasmussen from the University of Cambridge and the grading committee consists of Docent Tatjana Pavlenko (KTH), Docent Carl Henrik Ek, (University of Bristol) and Professor Kaj Nyström (Uppsala University). Welcome!

September 1, 2018 [Paper accepted for Nuclear inst. and methods in physics research section B] In this paper we present our new results on modelling and reconstruction of strain fields, relying upon data generated from neutron Bragg-edge measurements. This is an application of our Gaussian process construction from NIPS 2017 that correctly accounts for known linear constraints. More specifically the strain field is modelled as a Gaussian process, assigned a covariance structure customised by incorporation of the so-called equilibrium constraints. The results indicate a high potential and we hope that this will inspire the concept of probabilistic modelling to be used within other tomographic applications as well.

Carl Jidling, Johannes Hendriks, Niklas Wahlström, Alexander Gregg, Thomas B. Schön, Chris Wensrich and Adrian Wills. Probabilistic modelling and reconstruction of strain. Nuclear instruments and methods in physics research section B, 2018. [arXiv]


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 © Thomas Schön 2018