Thomas Schön

schon2020

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 28, 2022 [Two new papers accepted] The sampling and resampling steps of the particle filter are challenging to differentiate. The reparameterisation trick was introduced to allow sampling steps to be reformulated as differentiable functions. We have extend the reparameterisation trick to include the stochastic input to allow for better differentiation of the resampling step. This allows us to compute gradients which we can then use for example together with particle Markov Chain Monte Carlo (p-MCMC) and the No-U-Turn Sampler (NUTS) as the proposal when estimating parameters. In the second paper we offer some insights into how nonlinear state-space models and the particle filter can be used to predict political violence using data from our colleagues at the Department of Peace and Conflict Research, the ViEWS project.

Conor Rosato, Paul Horridge, Thomas B. Schön and Simon Maskell. Efficient learning of the parameters of non-linear models using differentiable resampling in particle filters. IEEE Transactions on Signal Processing, 2022.

Andreas Lindholm, Johannes Hendriks, Adrian Wills and Thomas B. Schön. Predicting political violence using a state-space model. International Interactions, 2022.

May 12, 2022 [Three new papers accepted] Recently the following three papers have been accepted. 

Li-Hui Geng, Adrian Wills , Brett Ninness and Thomas B. Schön. Smoothed state estimation via efficient solution of linear equations. IEEE Transactions on Automatic Control, 2022.

Carl Jidling, Adrian Wills, Andrew Flemming and Thomas B. Schön. Memory Efficient Constrained Optimization of Scanning-Beam Lithography. Optics Express, 2022. [pdf]

Niklas Gunnarsson, Jens Sjölund, Peter Kimstrad and Thomas B. Schön. Unsupervised dynamic modeling of medical image transformations. The 25th International Conference on Information Fusion, July, 2022, Linköping, Sweden.

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 © Thomas Schön 2022