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Resume (English only)
Academic Achievements
July 2025: Co-authored a new book on data-driven control with Kanat Camlibel and Harry Trentelman
Pioneered the 'data informativity' framework to address how much data is required for control; see the new book and overview paper 'The informativity approach to data-driven analysis and control'
Developed generalizations of the S-lemma and Finsler's lemma for robust controller design under noise and bounded nonlinearities, with key papers including 'Quadratic matrix inequalities with applications to data-based control', 'From noisy data to feedback controllers: non-conservative design via a matrix S-lemma', and 'A matrix Finsler's lemma with applications to data-driven control'
Published work on kernel-based modeling for (incrementally) dissipative systems, such as 'Towards a representer theorem for identification of passive systems', 'Kernel-based models for system analysis', and 'Training Lipschitz continuous operators using reproducing kernels'
Contributed to experiment design with papers including 'The shortest experiment for linear system identification', 'A persistency of excitation condition for continuous-time systems', and 'Beyond persistent excitation: online experiment design for data-driven modeling and control'
Research Experience
Assistant professor in the Systems, Control and Optimization group at the University of Groningen
June 2025: Delivered a lecture at the DISC summer school on experiment design for data-driven modeling and control
April 2025: Co-lectured a 5-day course at IIT Mandi, India
December 2024: Organized a workshop on data-driven control at the CDC in Milan
March 2024: Gave a mini-course on data-driven control at the 2024 Benelux meeting on Systems and Control
Background
Assistant professor at the University of Groningen, Netherlands
Member of the Systems, Control and Optimization group at the Bernoulli Institute for Mathematics, Computer Science and AI
Also affiliated with the Centre for Data Science and Systems Complexity and the Jan C. Willems Center for Systems and Control
Research focuses on developing systems and control theory grounded in measured data, aiming to map raw data into models and control policies with rigorous guarantees on accuracy, stability, and performance
Key research questions include: how much data is needed, how to handle noise, and how to design control-relevant experiments
Specific research topics: direct data-driven control, system identification, kernel-based modeling of (physical) dynamical systems, experiment design, applications to networked systems and neuromorphic computing