Thomas Schön

Thomas Schön, Professor of Automatic Control at Uppsala University. Photo: Mikael Wallerstedt

My main research interest is nonlinear inference, especially within the context of dynamical systems, solved using probabilistic models and algorithms. In terms of scientific fields, my research is situated somewhere on the intersection between the fields of machine learning, signal processing and automatic control. My aim is to pursue both basic and applied research, where the latter is typically carried out in close collaboration with industry. A brief overview of my research is available here and my publications are available here.

Recent research results/news

June 10, 2016: We (Pierre Jacob, Fredrik Lindsten and myself) have developed a new coupling construction for particle filters and conditional particle filters. The coupled particle filter improves the performance significantly for example when it comes to computing gradients and using it inside the particle Metropolis Hastings algorithm. When the coupled conditional particle filter is combined with a recent debiasing technique we obtain a new smoothing strategy with the appealing properties of 1) straightforward parallelization and 2) the construction of accurate error estimates. Neither of the above is possible with existing particle smoothers. All the details are available in this arXiv:ed paper:

Pierre E. Jacob, Fredrik Lindsten and Thomas B. Schön. Coupling of Particle Filters. Pre-print arXiv, June, 2016. [arXiv]

May 16, 2016: I gave an interview about our new ASSEMBLE project, you can find it here.

May 10, 2016: We have three new papers accepted, where we present new algorithms for the calibration of magnetometers and magnetometers and a scalable and distributed solution to inertial motion capture, respectively.

Manon Kok and Thomas B. Schön. Magnetometer calibration using inertial sensorsIEEE Sensors Journal, 2016.

Manon Kok, Sina Khoshfetrat Pakazad, Thomas B. Schön, Anders Hansson and Jeroen D. Hol. A scalable and distributed solution to the inertial motion capture problem. Submitted to the 19th International conference on information fusion, Heidelberg, Germany, June, 2016. [arXiv]

Fredrik Olsson, Manon Kok, Kjartan Halvorsen and Thomas B. Schön. Accelerometer calibration using sensor fusion with a gyroscope. In Proceedings of the IEEE Workshop on Statistical Signal Processing (SSP), Palma de Mallorca, Spain, June, 2016.

ssf logo

April 12, 2016: Our project ASSEMBLE goes live! We will work on automating machine learning and bringing powerful algorithms (like Sequential Monte Carlo) to the users. The project is funded by The Swedish foundation for strategic research. The partners are David Black-Schaffer (Uppsala university), Joakim Jaldén (KTH) and David Broman (KTH). Link to SSF announcement.

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 © Thomas Schön 2016