Our aim is to automate the extraction of knowledge and understanding from data. Allowing machines (and humans) to understand what is happening and to acquire new skills and learn new things. We achieve this by developing new probabilistic models and deriving algorithms capable of learnings these models from data. The systematic use of probability in representing and manipulating these models is key. It allows us to represent not only what we know, but to some extent also what we do not know. We take a particular interest in dynamical phenomena evolving over time.
Our research is multi-disciplinary and it sits somewhere on the intersection of the areas of Machine learning and statistics, signal processing, automatic control and computer vision. We pursue both basic and applied research, which explains our tight collaboration with various companies. A slightly more detailed overview of our research is available here.
Recent research results/news
January 23, 2017: [One finished and two new PhD students] Manon Kok successfully defended her PhD thesis and she is now moving on to the University of Cambridge where she will do her post-doc with Carl Rasmussen in the Machine Learning group. We also have two new PhD students joining the team; Muhammad Osama (starting as soon as his residence permit is grated) and Carl Jidling (starting on February 1). Really look forward to working with you, welcome to the team!
January 10, 2017: [Two updates on the Machine Learning activities in Uppsala] 1. We (Fredrik Lindsten, Andreas Svensson, Niklas Wahlström and myself) will offer a course in statistical Machine Learning designed for fourth year MSc students, starting on Monday next week. More information is available from the course web site. 2. The Machine Learning activities are covered by the arena page which is available here.
December 13, 2016: [New PhD thesis] Next month on Friday January 13 Manon Kok will defend her PhD thesis entitled Probabilistic modeling for sensor fusion with inertial measurements. The thesis is available here. The faculty opponent is Eric Foxlin (founder and builder of InterSense) currently working for fitbit. The thesis committe consists of Professor Fredrik Tufvesson (Lund University), Docent Isaac Skog (KTH), Docent Edith Ngai (Uppsala University) and as fall-back committee member we have Professor Magnus Herberthsson (Linköping University).
December 11, 2016: [New paper] Our new flexible nonparametric nonlinear state space model has been accepted for publication in Automatica. The model builds upon a basis function expansion and we use a connection to Gaussian processes to develop priors on the coefficients, for tuning the model flexibility and to prevent overfitting to data, akin to a Gaussian process state space model (GP-SSM). The priors can alternatively be seen as a regularization, and helps the model in generalizing the data without sacrificing the richness offered by the basis function expansion. An important part of the contribution is to show that it is possible to do regularization in learning nonlinear dynamical systems.
November 21, 2016: [New Licentiate thesis] On December 16 Andreas Svensson will present his licentiate thesis entitled "Learning probabilistic models of dynamical phenomena using particle filters” (available here). The discussion leader will be Richard Turner from the Machine Learning group at the University of Cambridge (UK).
Click here for older news.