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

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.

April 25, 2019 [Paper accepted for IEEE Access] The question we are interested in within this line of work is: “Is a model class consistent with the available data?”. We explore the simple idea of simulating data from the model and compare that (in terms of the likelihoods) to the available data. The end result is a very general (but computer intensive) approach! 

Andreas Lindholm, Dave Zachariah, Petre Stoica, and Thomas B. Schön. Data consistency approach to model validation. IEEE Access, 2019. (Accepted) [arXiv] [code]

April 22, 2019 [Paper accepted for ICML!] We are entering the field of causal inference and in June we will present our initial results at ICML. We investigate the problem of inferring the causal effect of an exposure on an outcome across space, using observational data. The data is possibly subject to unmeasured confounding variables which, in a standard approach, must be adjusted for by estimating a nuisance function. Here we develop a method that eliminates the nuisance function, while mitigating the resulting errors-in-variables. The result is a robust and accurate inference method for spatially varying heterogeneous causal effects.

Muhammad Osama, Dave Zachariah and Thomas B. Schön. Inferring heterogeneous causal effects in presence of spatial confoundingIn Proceedings of the 36th International Conference on Machine Learning (ICML), Long Beach, CA, USA, June, 2019. [arXiv]

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 © Thomas Schön 2019