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

March 22, 2019 [PhD course on Sequential Monte Carlo (SMC) methods] We will once again offer our PhD course on Sequential Monte Carlo (SMC) methods. The course is given as an intensive course during one week (August 26-29, 2019). Full information about the course is available here. Registration is now open! Last time gave the course (see picture to the left) there were roughly 80 participants from 35 different universities/companies from all around the world. 

We recently wrote a piece on how SMC can be leveraged in solving Machine Learning problems. We foresee a very interesting future here. A draft paper is available here.

Welcome to the course!!

February 8, 2019 [New PhD course in Deep Learning!] We will offer a PhD course in Deep Learning, starting next month. For more information, click here.

December 27, 2018 [Two papers accepted for AISTATS!] We will present new results at the 22nd International Conference on Artificial Intelligence and Statistics (AISTATS) in Naha (Japan) in April, 2019. The first paper presents a multiresolution Gaussian process (GP) model which assumes conditional independence among GPs across resolutions. The model is built on the hierarchical application of predictive processes using a particular representation of the GP via the Karhunen-Loeve expansion with a Bingham prior model. In the second paper we study model calibration in classification. A probabilistic classifier is said to be calibrated if the probability distributions that it outputs are consistent with the empirical frequencies observed in the measured data. We develop a rather general theoretical calibration evaluation framework for classification. We illustrate its use on standard deep learning classifiers.

Jalil Taghia and Thomas B. Schön. Conditionally independent multiresolution Gaussian processes. In Proceedings of the 22nd International Conference on Artificial Intelligence and Statistics (AISTATS), Naha, Japan, April, 2019. (Oral presentation) [arXiv]

Juozas Vaicenavičius, David Widmann, Carl Andersson, Fredrik Lindsten, Jacob Roll and Thomas B. Schön. Evaluating model calibration in classification. In Proceedings of the 22nd International Conference on Artificial Intelligence and Statistics (AISTATS), Naha, Japan, April, 2019. [arXiv]


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 © Thomas Schön 2019