Ivan Markovsky

Ivan Markovsky

Centre Internacional de Mètodes Numèrics a Enginyeria

Engineering Sciences

My Ph.D. is in electrical engineering from the Katholieke Universiteit Leuven, Belgium. From 2006 to 2012 I was a lecturer at the School of Electronics and Computer Science of the University of Southampton, U.K. and from 2012 to 2022 a research professor at the Vrije Universiteit Brussel, Belgium. My expertise is in system identification and data-driven control. In 2010, I was awarded an ERC starting grant for a structured low-rank approximation approach to data-driven control. Current topics of interest are data-driven methods for nonlinear, time-varying, and distributed systems.

Research interests

The objective of my research is unsupervised data-driven analysis and design of dynamical systems. The classical paradigm splits the problem into model identification and model-based design. In general, there is no separation principle for modeling and design, so that the two-stage approach may be suboptimal. I am investigating an alternative direct data-driven paradigm that combines modeling and design into one joint problem. In 2010, I proposed a solution approach for data-driven design based on structured low-rank approximation (ERC starting grant). More recently, I investigated convex relaxation, subspace, and regularization methods. Current topics of interest are data-driven methods for nonlinear, time-varying, and distributed systems. Besides data-driven design, I am interested in methods for teaching and learning that are effective in training critical thinking and creativity. I am an advocate of the open peer review as an alternative to the traditional closed review system.

Selected publications

- Markovsky I & Ossareh H 2024, 'Finite-data nonparametric frequency response evaluation without leakage', Automatica, 159, pp 111351.
- Wang J, Hemelhof L, Markovsky I & Patrinos P, 2024, 'A trust-region method for data-driven iterative learning control of nonlinear systems' Control Systems Letters, vol. 8, pp. 1847-1852.
- Markovsky I, Alsalti M, Lopez VG, Müller MA, 2024, 'Identification from data with periodically missing output samples', Automatica, 169, 111869.

Selected research activities

1. Collaboration on the KULeuven-ETH-Zurich WEAVE project (01/2022--12/2025). Main activities in 2024:
 - co-supervision of 3 PhD student and 1 postdoc at the IfA lab of ETH and the STADIUS group of KUL,
 - a visit to the IfA lab 01/07--31/08 for collaboration with the ETH team, and
 - two visits to the STADIUS group 11/02-14/02 and 06/06-10/06 for collaboration with the KUL team.
2. Submitted project (co-PI) "Behavioral Direct Data-Driven Control for Renewable Energy Systems" to the DFF Sapere Aude call. (collaboration with Prof. Saeed Golestan from Aalborg University, Denmark)
3. Accepted (PI) Generación de Conocimiento project "Model-based and data-driven monitoring methods for tailings dams" (see section Active Grants).
4. Invited lecture "Behavioral approach to system identification and data-driven control," Workshop on Data-Driven Control: Theory And Applications, at the Conference on Decision and Control, Milan, Italy, 15 December. 
5. Mini-course, UKACM-SEMNI Autumn school, 16-19 September (see https://imarkovs.github.io/ukacm/index.html).
6. IEEE-CSM best paper award (see https://ieeecss.org/awards/ieee-control-systems-magazine-outstanding-paper-award).
7. Associate editor of the International Journal of Control and reviewer for Automatica, IEEE Transactions on Automatic Control, and Control Systems Magazine.