|Zeit:||29. November 2022|
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Prof. Tobias Sutter
Department of Computer and Information Science
Machine Learning and Optimization
Tuesday 2022-11-29 4 p.m.
IST Seminar Room 2.255 - Pfaffenwaldring 9 - Campus Stuttgart-Vaihingen
Given the recent progress in information technology with real-time data being available at large scale, many complex tasks involving dynamical environments are addressed via tools from machine learning, control theory and optimization. While control theory in the past has mainly focused on model based design the advent of large scale data sets raises the possibility to analyse dynamical systems on the basis of data rather than analytical models. From a machine learning perspective, one of the main challenges going forward is to tackle problems involving dynamical systems which are beyond static pattern recognition problems. In this talk, I will give an overview about different problems lying in this intersection of dynamical systems, learning and control that I have worked on in the past. In particular, I will discuss how to efficiently learn a linear dynamical system with stability guarantees and how to identify its topological equivalence class based on a single trajectory of correlated data.
Tobias Sutter received a B.Sc. and M.Sc. degree in Mechanical Engineering in 2010 and 2012 from ETH Zürich, and a Ph.D. degree in Electrical Engineering at the Automatic Control Laboratory, ETH Zürich in 2017. He currently is an Assistant Professor at the Computer Science Department in Konstanz, Germany. Prior to joining University of Konstanz, he held a research and lecturer appointment with EPFL at the Chair of Risk Analytics and Optimization and at the Institute of Machine Learning at ETH Zürich. His research interests revolve around control, reinforcement learning and data-driven robust optimization. He was a recipient of the 2016 George S. Axelby Outstanding Paper Award from the IEEE Control Systems Society and received the ETH Medal for his outstanding Ph.D. thesis on approximate dynamic programming in 2018.