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.
October 19, 2015: Looking for 1-2 PhD students
I am looking for new PhD students to work on nonlinear modelling and inference/estimation problems arising in machine learning and system identification. More information on how to apply is available here (English) and here (Swedish). The deadline is December 20, 2015.
Recent research results/news
November 19, 2015: On December 4 two PhD students that I really enjoy working with will defend their theses. The title of Niklas Wahlström’s thesis is Modelling of magnetic fields and extended targets for localisation applications and it is available here and in DiVA. Professor Simon Maskell will act as the opponent. The title of Joel Kronander’s thesis is Physically based rendering of synthetic objects in real environments and it is available here and in DiVA. The opponent for Joel’s thesis will be Greg Ward.
Christian A. Naesseth, Fredrik Lindsten and Thomas B. Schön. Towards Automated Sequential Monte Carlo for Probabilistic Graphical Models. In Black box learning and inference workshop at the Conference on Neural Information Processing Systems (NIPS), Montréal, Canada, December 2015.
John-Alexander M. Assael, Niklas Wahlström, Thomas B. Schön and Marc Peter Deisenroth. Data-efficient learning of feedback policies from image pixels using deep dynamical models. In Deep Reinforcement Learning Workshop at the Conference on Neural Information Processing Systems (NIPS), Montréal, Canada, December 2015.
October 26, 2015: I will give my PhD course in Statistical Machine Learning again. The course starts in mid January. For more information about the course, click here. There is no formal registration required, but send me an e-mail is case you are intertested in participating in the course. Welcome!
September 11, 2015: Two new results on how to regularize nonlinear state space models and how to marginalize the hyper-parameters in Gaussian processes have been accepted for publication at the sixth IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP) that will be held in Cancun, Mexico in December 2015.
Andreas Svensson, Arno Solin, Simo Särkkä and Thomas B. Schön. Nonlinear state space model identification using a regularized basis function expansion. In Proceedings of the IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP), Cancun, Mexico, December 2015.
Andreas Svensson, Johan Dahlin and Thomas B. Schön. Marginalizing Gaussian process hyperparameters using sequential Monte Carlo methods. In Proceedings of the IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP), Cancun, Mexico, December 2015. [arXiv]
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