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, Eising J & Padoan A 2025, 'How to Represent and Identify Affine Time-Invariant Systems?', IEEE Control Systems Letters, vol. 9, pp. 1207--1212.
- Alsalti M, Markovsky I, Lopez VG & Müller MA 2025, 'Data-Based System Representations From Irregularly Measured Data', IEEE Trans. on Automatic Control, vol. 70, no. 1, pp 143-158.
- Usevich K, Gillard J, Dreesen P & Markovsky I 2025, 'Structured Nuclear Norm Matrix Completion: Guaranteeing Exact Recovery via Block-Column Scaling', Numerical Linear Algebra with Applications, vol. 32, no. 4, pp e70031.
- Yan J, Markovsky I & Lygeros J 2025, 'Secure data reconstruction: A direct data-driven approach', IEEE Trans. Automat. Contr., vol 70, no. 12, pp 8361-8367.
- Kaviani F, Markovsky I, and Ossareh H, 2025, 'Uncertainty Quantification of Data-Driven Output Predictors in the Output Error Setting', IEEE Trans. on Automatic Control, vol. 70, no. 11, pp. 7588-7595.

Selected research activities

- Research visit IfA group, ETH-Zurich, Switzerland
- Invited talk "Computations for systems and control without model parameters," 9/7, Selva di Fasano, Italy
- Invited talk "Low-Rank Approximation: Theory, Algorithms, and Applications", 26/8, Tübingen, Germany
- Invited lecture "Hidden structures in data-driven representations of dynamical systems", 19-23/5, L'Aquila, Italy
- PhD course "Data-driven systems theory, signal processing, and control", FRONTIERS network, 16/6, Barcelona
- Collaboration with M. Mitchell (ICFO) and PhD committee member for H. Raghavan 
- PhD thesis defense of R. Wang, (examiner), 19 September, KULeuven, Belgium
- Symposium "Recent Progress in Direct Data-Driven Methods" (organizer), ADMOS, 11/6, Barcelona