Speakers

Thomas Beckers

Thomas Beckers is an Assistant Professor of Computer Science and the Institute for Software Integrated Systems at Vanderbilt University. Before joining Vanderbilt, he was a postdoctoral researcher at the Department of Electrical and Systems Engineering, University of Pennsylvania, where he was member of the GRASP Lab, PRECISE Center and ASSET Center. In 2020, he earned his doctorate in Electrical Engineering at the Technical University of Munich (TUM), Germany. He received the B.Sc. and M.Sc. degree in Electrical Engineering in 2010 and 2013, respectively, from the Technical University of Braunschweig, Germany. In 2018, he was a visiting researcher at the University of California, Berkeley.

He is a DAAD AInet fellow and was awarded with the Rhode & Schwarz Outstanding Dissertation prize. His research interests include physics-enhanced learning, nonparametric models, and safe learning-based control. [Website]

Maik Pfefferkorn

Maik Pfefferkorn obtained his Bachelor’s and Master’s degrees in biosystems engineering from the Otto-von-Guericke University Magdeburg in 2018 and 2020, respectively. Since 2020, he is a Ph.D. student in Rolf Findeisen’s group at the Otto-von-Guericke University Magdeburg.

Maik’s research interests are in the theory and application of machine learning approaches, especially Gaussian process regression, in model predictive control with stability and safety guarantees. [Website]

Armin Lederer

Armin Lederer received the B.Sc. and M.Sc. degree in electrical engineering and information technology from the Technical University of Munich, Germany, in 2015 and 2018, respectively. Since 2018, he is at the Chair of Information-oriented Control at the Technical University of Munich, Germany, where he has pursued a PhD until 2023. His current research focuses on the design of Gaussian process-based control and learning approaches for certifiably safe learning in closed-loop control systems.

Colin Jones

Colin Jones is an Associate Professor in Mechanical Engineering at the EPFL (Ecole Polytechnique Federale de Lausanne) in Switzerland. He received bachelor and master degrees in Electrical Engineering at the University of British Columbia in Canada in 1999 and 2001 before obtaining a Ph.D. in 2005 from the University of Cambridge for his work on polyhedral computational methods for constrained control. He spent five years at the ETH Zurich as a senior researcher studying parametric optimization and predictive control, before joining the EPFL as an assistant professor. He has served as an associate editor for the IEEE Transactions on Control Systems Technology, the Systems and Control Letters and the journal Optimal Control Applications and Methods and is an author of over 200 journal and conference papers. He is a recipient of the ERC Starting Grant, which was awarded to study the control of networks of buildings for providing demand response services. His current research interests are in the areas of high-speed and large-scale predictive control and optimization, as well as green energy generation, distribution and management. [Website]

Karl Berntorp

Karl Berntorp received the M.Sc. degree in engineering physics and the Ph.D. degree in automatic control from Lund University, Lund, Sweden, in 2009 and 2014, respectively.

In 2008, he was a Visiting Researcher with Daimler AG, Sindelfingen, Germany. Since 2014, he has been with Mitsubishi Electric Research Laboratories, Cambridge, MA, USA. His work includes the design and implementation of estimation, constrained control, motion planning, and learning algorithms. He is the author of more than 80 papers in journals and conferences and has more than 20 granted patents. His research interests include statistical signal processing, Bayesian inference, sensor fusion, and optimization-based control, with applications to automotive, transportation, navigation, satellite-based positioning, and communication systems. Dr. Berntorp is a Senior Member of IEEE and an Associate Editor and member of the IEEE Technology Conferences Editorial Board. [Website]

Sandra Hirche

Sandra Hirche holds the TUM Liesel Beckmann Distinguished Professorship and heads the Chair of Information-oriented Control in the Faculty of Electrical and Computer Engineering at Technical University of Munich (TUM), Germany (since 2013). She received the diploma engineer degree in Aeronautical and Aerospace Engineering in 2002 from the Technical University Berlin, Germany, and the Doctor of Engineering degree in Electrical and Computer Engineering in 2005 from the Technische Universität München, Munich, Germany. From 2005-2007 she has been a PostDoc Fellow of the Japanese Society for the Promotion of Science at the Fujita Laboratory at Tokyo Institute of Technology, Japan. Prior to her present appointment she has been an Associate Professor at TUM.

Her main research interests include learning, cooperative, and networked control with applications in human-robot interaction, multi-robot systems, and general robotics. She has published more than 200 papers in international journals, books and refereed conferences. She has received multiple awards such as the IFAC World Congress Best Poster Award in 2005 and – together with students – several best paper awards including the Outstanding Student Paper Award of the IEEE Conference on Decision and Control 2018. In 2013 she has been awarded with an ERC Starting Grant on the “Control based on Human Models” and in 2019 with the ERC Consolidator Grant on “Safe data-driven control for human-centric systems”.

Sandra Hirche is Fellow of the IEEE. She received the IEEE CSS Distinguished Member Award in 2021. She has served as IEEE Control System Society (CSS) Vice-President for Member Activities (2014/15), as Chair for Student Activities in the IEEE CSS (2009-2014), as Chair of the CSS Awards Subcommittee on “CDC Best Student-Paper Award” (2010-2014), and has been elected member of the Board of Governors of IEEE CSS (2010-2013). Furthermore she has been Co-Chair of the IFAC TC 1.5 “Networked Control Systems” (2010-2017) and IPC Co-Chair of the IFAC World Congress 2020. [Website]

Rolf Findeisen

Rolf Findeisen studied engineering cybernetics at the University of Stuttgart and chemical engineering at the University of Wisconsin – Madison. He began his doctoral studies at ETH Zurich's, which he completed in 2004 following his doctoral father to the University of Stuttgart. 2007, Rolf was appointed professor at the Institute of Automatic Control at Otto-von-Guericke University Magdeburg. Since August 2021, he heads the Control and Cyber-physical Systems Laboratory at the Technical University of Darmstadt.

Rolf is engaged in method development in the area of systems theory and control engineering, focusing on optimization-based and predictive control; fusing machine learning approaches such as Gaussian processes and neural networks with model based control providing guarantees; and control of complex, distributed systems via communication networks. [Website]

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