Date & Venue
Wednesday, 20 November 2019, 11:00am
EF122 

Abstract
This talk has two (for now) loosely connected parts: In the first part we aim to provide intuition for the key mechanisms underlying the sequential Monte Carlo (SMC) method (including the popular particle filters and smoothers). SMC provide approximate solutions to integration problems where there is a sequential structure present. The classical example of such a structure is offered by nonlinear dynamical systems, but we stress that SMC is significantly more general than most of us first thought. We will hint at a few ways in which SMC fits into the machine learning toolbox and mention a few interesting avenues for research. In the second part we develop a new approach to deep regression. While deep learning-based classification is generally addressed using standardized approaches, a wide variety of techniques are employed when it comes to regression. We have developed a new and general deep regression method with a clear probabilistic interpretation. We obtain good performance on several computer vision regression tasks (including a new state-of-the-art result on visual tracking). The loose connection lies in the use of the Monte Carlo idea in both topics. We do believe that the connection between the two seemingly disparate topics will be strengthened over the coming years.

Speaker Biography

Thomas B. Schön is Professor of the Chair of Automatic Control in the Department of Information Technology at Uppsala University. He received the PhD degree in Automatic Control in Feb. 2006, the MSc degree in Applied Physics and Electrical Engineering in Sep. 2001,  the BSc degree in Business Administration and Economics in Jan. 2001, all from Linköping University. He has held visiting positions with the University of Cambridge (UK), the University of Newcastle (Australia) and Universidad Técnica Federico Santa María (Valparaíso, Chile). In 2018, he was elected to The Royal Swedish Academy of Engineering Sciences (IVA) and The Royal Society of Sciences at Uppsala. He received the Tage Erlander prize for natural sciences and technology in 2017 and the Arnberg prize in 2016, both awarded by the Royal Swedish Academy of Sciences (KVA). He was awarded the Automatica Best Paper Prize in 2014, and in 2013 he received the best PhD thesis award by The European Association for Signal Processing. He received the best teacher award at the Institute of Technology, Linköping University in 2009. He is a Senior member of the IEEE and a fellow of the ELLIS society.

Schön has a broad interest in developing new algorithms and mathematical models capable of learning from data. His  main scientific field is Machine Learning, but he also regularly publishes in other fields such as Statistics, Automatic Control, Signal Processing and Computer Vision. He pursues both basic research and applied research, where the latter is typically carried out in collaboration with industry or applied research groups. More information about his research can be found on his website.