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.
Up to 5 PhD positions in interdisciplinary mathematics. More information is available from CIM and the link to the application portal is available here. There are also some concrete project suggestions available here. The project I am involved in deals with data analytics in mobile communication networks together with Kaj Nyström and partners from Ericsson, more information is available here. Deadline for application is February 14, 2016.
Recent research results/news
January 18, 2016: The 5th edition of the Statistical Machine Learning (SML) course starts tomorrow. The theme of this year’s projects is high-energy particle physics. Some interactions between the fields are available from the dedicated workshops held at NIPS in 2015 (here) and 2014 (here).
January 15, 2016: During the 41st IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) to be held in Shanghai, China, I will give a 1 day (March 20) tutorial on how to use sequential Monte Carlo (e.g. particle filters) methods to learn models of nonlinear dynamical systems. The title of the tutorial is Learning nonlinear dynamical models using particle filters and a brief abstract is available here. I will make the slides available before the conference. The tutorial is built around this paper,
Thomas B. Schön, Fredrik Lindsten, Johan Dahlin, Johan Wågberg, Christian A. Naesseth, Andreas Svensson and Liang Dai. Sequential Monte Carlo methods for system identification. In Proceedings of the 17th IFAC Symposium on System Identification (SYSID), Beijing, China, October 2015. [pdf] [arXiv] [Code]
December 22, 2015: Our construction of a new Gaussian process state space model enabling Bayesian inference in nonlinear dynamical systems has been accepted for publication at the 19th International Conference on Artificial Intelligence and Statistics (AISTATS) that will be held in Cadiz (Spain) in May, 2016.
Andreas Svensson, Arno Solin, Simo Särkkä and Thomas B. Schön. Computationally efficient Bayesian learning of Gaussian process state space models. In Proceedings of the 19th International Conference on Artificial Intelligence and Statistics (AISTATS), Cadiz, Spain, May, 2016. [arXiv]
December 11, 2015: The Linear Quadratic Gaussian (LQG) controller has been around for a long time and it is a very commonly used controller. Somewhat surprisingly, the statistical properties of the resulting cost function have previously not been fully sorted out. We have derived analytic expressions for the variance (and also the mean) of the LQG cost function. Besides adding to the understanding of the properties of the controller, this also opens up for new controller synthesis. The results have been accepted for Automatica.
Hildo Bijl, Jan-Willem van Wingerden, Thomas B. Schön and Michel Verhaegen. Mean and variance of the LQG cost function. Automatica, 2016.
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.
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