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.
February 7, 2017 [Looking for 2 PhD students in Machine Learning (Deep Learning)] We are looking for two PhD students in Machine Learning working closely with two of our industrial partners (Autoliv and Sectra). This is an effort to further expand our research in Deep Learning and to extend our collaborations with Autoliv and Sectra. More information is available here (jointly with Autoliv) and here (jointly with Sectra). The formal applications are submitted here.
Recent research results/news
January 25, 2017 [New paper accepted for AISTATS 2017] The backgound to this paper is that the existing uncertainty estimates for Gaussian processes employing hyperparameters that are learnt from data are actually wrong. The contribution of the paper is that we derive a correct and fundamental lower bound of the mean square error (MSE). Importantly this bound is available as a closed-form expression that is simple and cheap to compute, so whenever you need to compute the uncertainty from a Gaussian process you should use these results. The paper will be presented at the 20th International Conference on Artificial Intelligence and Statistics (AISTATS) held in Fort Lauderdale, FL, USA in April, 2017.
Johan Wågberg, Dave Zachariah, Thomas B. Schön and Petre Stoica. Prediction performance after learning in Gaussian process regression. In Proceedings of the 20th International Conference on Artificial Intelligence and Statistics (AISTATS), Fort Lauderdale, FL, USA, April, 2017.
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).
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