Thomas B. Schön

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.

Recent Research Results/News

• June 17, 2013: Our new tutorial on particle smoothing methods is now accepted for publication in Foundations and Trends in Machine Learning,
  • [1] Fredrik Lindsten and Thomas B. Schön. Backward simulation methods for Monte Carlo statistical inference. Foundations and Trends in Machine Learning, 2013.
  This paper is written with the intent to be a self-contained introduction to particle smoothing in a general setting, not just confined to standard nonlinear state space models. More precisely, we discuss using forward filtering/backward simulation techniques for addressing both smoothing and parameter inference problems. Feel free to send me an e-mail if you want a pre-print.
• May 28, 2013: Johan Kihlberg and Simon Tegelid have been awarded the best Master's thesis award 2012 by Dataföreningen - öst. The thesis is entitled "Map Aided Indoor Positioning" and it is available for download by clicking here.
• May 22, 2013: I have been awarded the Best PhD thesis award 2013 by The European Association for Signal Processing (EURASIP). "The selection process is based on the evaluation of the impact of the theses, their subsequent journal and conference publications and related citations received, and on the review reports of three independent reviewers as well as on the download statistics." The thesis entitled "Estimation of Nonlinear Dynamic Systems: Theory and Applications" is available here and the announcement is available here.
• Apr 22, 2013: I am very glad to welcome Roger Frigola, who is visiting our team for a couple of months. Roger is a PhD student with the Machine learning group at the University of Cambridge (UK).
• Mar 7, 2013: Our new method for Wiener system identification has been accepted for publication in Automatica. The method avoids making strong assumptions, such as monotonicity, on the nonlinear mapping. Stochastic disturbances, entering both as measurement noise and as process noise, are also straightforwardly handled. The inference algorithm is based on an efficient particle Markov chain Monte Carlo (PMCMC) method, referred to as particle Gibbs with ancestor sampling (PG-AS).
  • Fredrik Lindsten, Thomas B. Schön and Michael I. Jordan. Bayesian semiparametric Wiener system identification. Automatica 2013. (accepted for publication) [pdf]

• Mar 1, 2013: We have some new results on the following topics; particle smoothing, PMCMC, modeling of magnetic disturbances and localization using magnetic maps. The underlying papers have been accepted for publication at the 38th International Conference on Acoustics, Speech, and Signal Processing (ICASSP) to be held in Vancouver, Canada during May 26-31, 2013.
  • [1] Fredrik Lindsten, Pete Bunch, Simon J. Godsill and Thomas B. Schön. Rao-Blackwellized particle smoothers for mixed linear/nonlinear state-space models. Proceedings of the 38th International Conference on Acoustics, Speech, and Signal Processing (ICASSP), Vancouver, Canada, May 2013. [pdf]
  • [2] Manon Kok, Niklas Wahlström, Thomas B. Schön and Fredrik Gustafsson. MEMS-based inertial navigation based on a magnetic field map. Proceedings of the 38th International Conference on Acoustics, Speech, and Signal Processing (ICASSP), Vancouver, Canada, May 2013. [pdf]
  • [3] Niklas Wahlström, Manon Kok, Thomas B. Schön and Fredrik Gustafsson. Modeling magnetic fields using Gaussian processes. Proceedings of the 38th International Conference on Acoustics, Speech, and Signal Processing (ICASSP), Vancouver, Canada, May 2013. [pdf]
  • [4] Johan Dahlin, Fredrik Lindsten and Thomas B. Schön. Particle Metropolis Hastings using Langevin dynamics. Proceedings of the 38th International Conference on Acoustics, Speech, and Signal Processing (ICASSP), Vancouver, Canada, May 2013. [pdf]
  • [5] Ehsan Taghavi, Fredrik Lindsten, Lennart Svensson and Thomas B. Schön. Adaptive stopping for fast particle smoothing. Proceedings of the 38th International Conference on Acoustics, Speech, and Signal Processing (ICASSP), Vancouver, Canada, May 2013. [pdf]