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
The group is expanding! We are hiring an Associate Professor in Signal Processing. The formal advertisment will be available shortly. Feel free to send me an e-mail if you have any questions.
December 17, 2014: We have been invited to present our optimization-based solution to the human body motion capture problem at the conference on Technically Assisted Rehabilitation (TAR 2015) held in Berlin, Germany in March 2015.
Manon Kok, Jeroen Hol and Thomas B. Schön. An optimization-based approach to human body motion capture using inertial sensors. Conference on Technically Assisted Rehabilitation (TAR), Berlin, Germany, March, 2015. (invited paper). A more complete description of this work is available here.
December 8, 2014: In june next year I will teach a module on nonlinear system identification using sequential Monte Carlo during the Summer school on stochastic filtering to be held in Barcelona, Spain.
November 12, 2014: Manon Kok will do her pre-doc together with Simo Särkkä at Aalto University in Finland during the time period January - March 2015.
September 22, 2014: Our new derivation of recursive direct weight optimization has been accepted for publication in IEEE TAC
Liang Dai and Thomas B. Schön. A new structure exploiting derivation of recursive direct weight optimization. IEEE Transactions on Automatic Control, 2014. (accepted for publication)
September 9, 2014: Our paper on inference in general probabilistic graphical models has been accepted for publication at NIPS 2014. Our method is consistent and it provides unbiased estimates of the partition function. I gave a seminar on the idea at the Isaac Newton Institute for Mathematical Sciences in Cambridge earlier this year, the talk is available here.
Christian A. Naesseth, Fredrik Lindsten and Thomas B. Schön. Sequential Monte Carlo methods for graphical models. Advances in Neural Information Processing Systems (NIPS) 27, Montreal, Quebec, Canada, December, 2014. [pdf] [arXiv] [code] [video]
Click here for older news.