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
November 21, 2016: 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).
October 7, 2016: On Tuesday next week Lawrence Murray will join us as a researcher working on the ASSEMBLE project. Lawrence moved from the University of Oxford where he just finished his post-doc together with Arnaud Doucet. Welcome to the team!
September 5, 2016: Our project Machine learning for diagnostic support in radiology together with Sectra was granted by Vinnova. This allows us to start up a joint industrial post-doc project on this topic.
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