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
May 28, 2014: Our recent explorations on the use of Monte Carlo methods (including sequential Monte Carlo) for real-time video based lightning has been accepted for publication at the 22nd European Signal Processing Conference (EUSIPCO) held in Lisabon, Portugal in September. This is very much introductory work for us and more will follow.
Joel Kronander, Johan Dahlin, Daniel Jönsson, Manon Kok, Thomas B. Schön and Jonas Unger. Real-time video based lighting using GPU raytracing, in Proceedings of the 22nd European Signal Processing Conference (EUSIPCO), Lisabon, Portugal, September 2014. (accepted for publication)
May 21, 2014: We (Nicolas Chopin, Adam Johansen and myself) are organizing a workshop on sequential Monte Carlo methods at ENSAE in Paris during August 26-28 2015. More information is available here. Hope to see you there!
May 9, 2014: On June 5, Manon Kok will defend her licentiate thesis entitled Probabilistic modeling for positioning applications using inertial sensors [pdf]. The discussion leader will be Dr. Gabriele Bleser from the Augmented Vision group at the German Research Center for Artificial Intelligence. The thesis contains (among other things); A new optimization-based formulation of human motion capture (finding the position and orientation of human bodies) using inertial sensors and a new algorithm to calibrate magnetometers in the presence of metallic objects. The thesis is available here.
May 5, 2014: On May 28, Johan Dahlin will defend his licentiate thesis entitled Sequential Monte Carlo for inference in nonlinear state space models [pdf]. The discussion leader will be Dr. Adam Johansen from the Department of Statistics at the University of Warwick. The thesis contains (among other things): new methods for learning parameters in nonlinear dynamical models and it also studies the input design problem for nonlinear dynamical systems with some new insights. The thesis is available here.
April 24, 2014: We have two new papers accepted for the 2014 IEEE Workshop on Statistical Signal Processing (SSP) to be held at Jupiters on the Gold Coast in Australia in July. The first result is a new marginal particle smoother that is based on running a sequential Monte Carlo sampler backward in time after an initial forward filtering pass. In the second paper we make use of mixture importance sampling ideas to derive robust and efficient particle filters.
Joel Kronander and Thomas B. Schön. Robust auxiliary particle filters using multiple importance sampling. Proceeding of the IEEE Statistical Signal Processing Workshop (SSP), Gold Coast, Australia, July 2014. (accepted for publication). [pdf]
Joel Kronander, Thomas B. Schön and Johan Dahlin. Backward sequential Monte Carlo for marginal smoothing. Proceeding of the IEEE Statistical Signal Processing Workshop (SSP), Gold Coast, Australia, July 2014. (accepted for publication). [pdf]
March 25, 2014: We have recently developed a new algorithm for inference in latent variable models, state space models (both Markovian and non-Markovian) being one important special case. The algorithm belongs to the family of particle MCMC (PMCMC) algorithms. More specifically our algorithms builds on the Particle Gibbs sampler of Christophe Andrieu, Arnaud Doucet and Roman Holenstein available here. The work has been accepted for publication in the Journal of Machine Learning Research (JMLR)
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