Thomas Schön

schon2020

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

June 10, 2021 [Paper accepted for the IEEE Control Systems Letters L-CSS] The main contribution of this paper is the formulation of a general control problem where the emphasis is moving from system data to control action. We assume that the system can be modelled by a nonlinear state-space model, but where the model parameters, state and future disturbances are not known and are treated as random variables. Central to our formulation is that the joint distribution of these unknown objects is conditioned on the observed data. Crucially, as new measurements become available, this joint distribution continues to evolve so that control decisions are made accounting for uncertainty as evidenced in the data. The resulting problem is intractable which we obviate by providing approximations that result in finite dimensional deterministic optimisation problems.

Johannes Hendriks, James Holdsworth, Adrian Wills, Thomas B. Schön and Brett Ninness. Data to controller for nonlinear systems: an approximate solution. IEEE Control Systems Letters (L-CSS), 2021. (accepted for publication)

May 4, 2021 [Paper accepted for the IEEE Control Systems magazine] In this paper we offer a contemporary introduction to sequential Monte Carlo (SMC) and in particular its use for nonlinear system identification. 

Anna Wigren, Johan Wågberg, Fredrik Lindsten, Adrian Wills and Thomas B. Schön. Nonlinear system identification – Learning while respecting physical models using Sequential Monte Carlo. IEEE Control Systems Magazine, 2021. (accepted for publication)

April 29, 2021 [Join our team - up to three postdoc positions on fundamental ML] You will enter a very active team within AI/ML. Basic research in Machine Learning - models and methods. More information (including instructions on how to apply) is available here.

April 16, 2021 [Paper for the CVPR Workshop on autonomous driving] Accurate 3D object detection (3DOD) is crucial for safe navigation of complex environments by autonomous robots. We address this challenging problem by exploring recent advances in conditional energy-based models (EBMs) for probabilistic regression. We design a differentiable pooling operator for 3D bounding boxes, serving as the core module of our EBM network. 

Fredrik K. Gustafsson, Martin Danelljan and Thomas B. Schön. Accurate 3D object detection using energy-based models. In the Workshop on Autonomous Driving (WAD) at the Conference on Computer Vision and Pattern  Recognition (CVPR), Online, 2021. [arXiv]


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 © Thomas Schön 2021