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

July 4, 2019 [Second paper on neutron transmission measurements accepted] Our first paper on the topic is available here. The existing algorithms for reconstructing the strain field from energy-resolved neutron transmission measurements all assume that the  strain-free lattice spacing d0 is a known constant limiting their application. We consider the more general problem of jointly reconstructing the strain- and the d0-fields. By utilizing recent  developments in Machine Learning we have developed a method for solving this inherently nonlinear problem. Our solution also ensures that the estimated strain field satisfies the equilibrium conditions imposed by physics. 

[J46] Johannes N. Hendriks, Carl Jidling, Thomas B. Schön, Adrian Wills, Christpher M. Wensrich and Erich H. Kisi. Neutron Transmission Strain Tomography for Non-Constant Strain-Free Lattice Spacing. Nuclear instruments and methods in physics research section B, 2019. (accepted) [arXiv]

June 28, 2019 [Paper accepted for Inverse Problems] This is our first (but certainly not last) work on x-ray computed tomography (CT) imaging, which is a non-invasive method to recover the internal structure of an object by collecting projection data from multiple angles. We use a Gaussian process prior to model the target function and estimate its (hyper)parameters from measured data (when we only have limited data available). In contrast to established methods, this comes with the advantage of not requiring any manual parameter tuning, which usually arises in classical regularization strategies. Our approach allows for reformulation of come classical regularization methods as Laplacian and Tikhonov regularization as Gaussian process regression, and hence provides an efficient algorithm and principled means for their parameter tuning.

Zenith Purisha, Carl Jidling, Niklas Wahlström, Thomas B. Schön and Simo Särkkä. Probabilistic approach to limited-data computed tomography reconstruction. 2019. [arXiv]

June 18, 2019 [Paper accepted for IEEE Transactions on Signal Processing] I have been working on Sequential Monte Carlo (SMC) methods since I started my PhD back in December 2001 and this is definitely one of the most interesting developments I have been involved in (so far :-) ) when it comes to SMC. A key challenge is to extend the algorithm to high-dimensional spaces. In this work we take one step in this direction by developing a construction that generalises the SMC framework by requiring only approximate, properly weighted, samples from the SMC proposal distribution, while still resulting in a correct SMC algorithm! This opens the door to completely new (higher dimensional) application areas, such as for example nonlinear spatio-temporal state space models.

Christian A. Naesseth, Fredrik Lindsten and Thomas B. Schön. High-dimensional filtering using nested sequential Monte CarloIEEE Transactions on Signal Processing, 2019. (Accepted) [arXiv]


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 © Thomas Schön 2019