Thomas Schön

Thomas Schön serves as the Beijer Professor of Artificial Intelligence at Uppsala University's Department of Information Technology within the Faculty of Science and Technology. His research focuses on developing probabilistic models and algorithms for extracting knowledge from data, with particular emphasis on dynamical systems.

His research interests span multiple disciplines at the intersection of Machine Learning and statistics, signal processing, automatic control, and computer vision. He takes a systematic approach to representing and manipulating uncertainty through probability theory. His work encompasses both basic and applied research, with strong collaborations with industry partners including ABB Crane Systems, Autoliv, Saab, Sectra, and Xsens Technologies.

Schön's research output demonstrates significant contributions to probabilistic modeling of dynamical systems, with particular expertise in sequential Monte Carlo methods (particle filters), Markov chain Monte Carlo, Gaussian processes, and deep learning. His recent work includes advancements in incorporating background knowledge into machine learning models and developing flexible probabilistic frameworks.

Schön actively supervises numerous PhD students working on diverse topics including Bayesian nonparametric models, deep learning applications, uncertainty-aware systems, and probabilistic computer vision. His research is funded by The Swedish Research Council (VR), The Swedish Foundation for Strategic Research (SSF), and Vinnova.