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

December 5, 2018 [Paper accepted for Automatica] As a follow-up on our previous paper on some of the fundamantal properties of Linear Quadratic Gaussian (LGQ) control we have now established attitional results related to the discounted-cost case. The situation is different in this case in that the cost function has an exponential discount factor, also known as a prescribed degree of stability. In this case, the optimal control strategy is only available when the state is fully known. Our new results extends this result by deriving an optimal control strategy when working with an estimated state. Expressions for the resulting optimal expected cost are also given. 

Hildo Bijl and Thomas B. Schön. Optimal controller/observer gains of discounted-cost LQG systems. Automatica, 2019. [pdf] [arXiv]

November 26, 2018 [Two spotlight presentations at NeurIPS next week!] Our team will give two spotlight presentations at the Conference on Neural Information Processing Systems (NeurIPS) in Montréal (Canada) next week:

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

Fredrik Lindsten, Jouni Helske and Matti Vihola. Graphical model inference: Sequential Monte Carlo meets deterministic approximationsIn Neural Information Processing Systems (NeurIPS), Montréal, Canada, December 2018.

November 20, 2018 [Paper accepted for the Machine Learning for Health Workshop at NeurIPS] We have shown that it is possible to achieve human level performance when it comes to ECG evaluation:

Antonio H. Ribeiro, Manoel Horta, Gabriela Paixao, Derick Oliveira, Paulo R. Gomes, Jessica A. Canazart, Milton Pifano, Wagner Meira Jr., Thomas B. Schön and Antonio Luiz Ribeiro. Automatic diagnosis of short-duration 12-lead ECG using a deep convolutional network. In ML4H: Machine Learning for Health Workshop at NeurIPS, Montréal, Canada, December 2018. [arXiv]

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).

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 © Thomas Schön 2018