Welcome

Thanks to all participants to the 2019-edition !!!!

Please consult the website of the 2023-edition (3-7 April 2023)

The link to the 2023-edition can be found here

2019 Spring School on Data-driven Model Learning of Dynamic Systems

Dates: Monday 1 April 2019 (14:00) - Thursday 4 April (evening)

The Spring School consists of a four-days PhD course on data-based modeling covering both the fundamentals and more advanced topics.

After two editions held in Nancy (in 2017 and in 2018), the third edition of the Spring School on Data-driven Model Learning of Dynamic Systems will be held at the Ecole Centrale de Lyon in the first week of April 2019 (April 1 - April 4, 2019). The campus of the Ecole Centrale de Lyon is located in Ecully, a town in the direct neighbourhood of Lyon.

Like in the previous editions, the 2019-edition will welcome an international guest lecturer.

INVITED GUEST LECTURER FOR THE 2019-EDITION

Prof. Håkan HJALMARSSON, KTH Stockholm, Sweden

presenting a one-day course entitled

Dynamic model learning

Biography of the guest lecturer: Håkan Hjalmarsson was born in 1962. He received the M.S. degree in Electrical Engineering in 1988, and the Licentiate degree and the Ph.D. degree in Automatic Control in 1990 and 1993, respectively, all from Linköping University,  Sweden. He has held visiting research positions at California Institute of Technology, Louvain University and at the University of Newcastle, Australia.  He has served as an Associate Editor for Automatica (1996-2001), and IEEE Transactions on Automatic Control (2005-2007) and been Guest Editor for European Journal of Control and Control Engineering Practice. He is Professor at the School of Electrical Engineering, KTH, Stockholm, Sweden. He is an IEEE Fellow and past Chair of the IFAC Coordinating  Committee CC1 Systems and Signals. In 2001 he received the KTH award for outstanding contribution to undergraduate education. His research interests include system identification, signal processing, control and estimation in communication networks and automated tuning of controllers.