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

November 9, 2018 [New PhD thesis to be defended] On December 14 at 10.15 Christian Naesseth will present and defend his PhD thesis. The title of the thesis is: Machine learning using approximate inference - Variational and sequential Monte Carlo methods. The thesis is available here. The opponent is Reader Ian Murray (University of Edinburgh) and the grading committee consists of Docent Jimmy Olsson (KTH). Docent Jose Peña (Linköping University) and Associate professor Morten Mørup (Technical University of Denmark).

October 30, 2018 [New pre-doc] I am very glad that we have Johannes Hendriks from The University of Newcastle (Australia) doing his pre-doc vith us during the period November 2018 - February 2019. We will be working on some fundamentals of the Gaussian process and in particular its application to tomographic reconstruction. For the latter we will continue the line of work we started here.

October 4, 2018 [Vision paper describing Birch accepted] This paper offers a broad perspective on probabilistic modeling and inference in light of recent advances in probabilistic programming, in which models are formally expressed in Turing-complete programming languages. We try to show how probabilistic programming languages can help to automate this workflow, especially in the matching of models with inference methods. The ideas are illustrated with a new probabilistic programming language called Birch.

Lawrence Murray and Thomas B. Schön. Automated learning with a probabilistic programming language: Birch. Annual Reviews in Control, 2018. (Accepted for publication) [arXiv]

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.

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 © Thomas Schön 2018