Thomas Schön

Thomas Schön, Professor of Automatic Control at Uppsala University. Photo: Mikael Wallerstedt

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

[Registration for the SMC workshop 2017 is open!] Sequential Monte Carlo (SMC) methods, also known as particle filters or particle methods, have over the past two decades emerged as very successful tools for computational inference in statistical models, including (but not limited to) nonlinear dynamical systems. The aim of the workshop is to bring together researchers developing and using SMC methods in various scientific fields (both in academia and industry). The list of speakers should make for a very interesting event; hope to see you there!

July 12, 2017 [Paper accepted for CDC] We have new results on the construction of probabilistic optimization algorithms (algorithms capable of solving optimization problems when we only have access to noisy versions of the cost function and its derivatives). It has recently been shown that most of the existing quasi-Newton algorithms can be formulated as learning algorithms, capable of learning local models of the cost function. We leverage this fact together with the non-parametric and probabilistic Gaussian process model in solving these stochastic optimization problems. Our new algorithm unites these approximations together with recent probabilistic line search routines to deliver a generally applicable probabilistic quasi-Newton approach.

Adrian G. Wills and Thomas B. Schön. On the construction of probabilistic Newton-type algorithms. In Proceedings of the 56th IEEE Conference on Decision and Control (CDC), Melbourne, Australia, December 2017. [arXiv]

July 5, 2017 [2 new post-docs joining the team] After the summer we have two new post-docs joining us. Juozas Vaicenavičius will be an industrial post-doc working on deep learning for autonomous driving together with our collaborators at Autoliv. Juozas is also affiliated with the center of interdisciplinary mathematics (CIM) here at Uppsala University. Jack Umenberger from the University of Sydney brings in important knowledge on optimization based methods for nonlinear system identification and machine learning and will be working in the ASSEMBLE project.

May 15, 2017 [Best paper award] Christian Naesseth received the best paper award at the 20th International Conference on Artificial Intelligence and Statistics (AISTATS). The award to given for this paper. Congratulations!! 


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 © Thomas Schön 2017