PhD students that I have been (co)advising, in rough reverse chronological order.

Current students

• Maria Ceapa, on global optimization.
• Mihalis Maer, coadvised with Zsofia Lendek, on intermittent-feedback state estimation.
• Tudor Alinei-Poiana, coadvised with Zsofia Lendek, on robust state estimation.
• Matthias Rosynski, on reinforcement learning for object search and coverage.
• David Rete, on active underwater mapping.
• Stefan Pirje, coadvised with Zsofia Lendek, on neural networks for control.
• Tudor Santejudean, on multi-agent optimization, cotutelle with Constantin Morarescu at the Centre de Recherche en Automatique de Nancy, France.
• Etienne Gorski, on cooperative platooning, cotutelle with Constantin Morarescu at the Centre de Recherche en Automatique de Nancy, France.
• Marius Dragomir, on learning for opinion dynamics.

Graduated students

• Bilal Yousuf (now a postdoc). Thesis: Exploration-Based Active Multi-Target Search using Single and Multiple Agents.
• Ioana Lal (graduated with the excellent qualifier). Thesis: Near-optimal control of nonlinear hybrid-input systems.
• Zoltan Nagy, coadvised with Zsofia Lendek (now a postdoc and teaching assistant). Thesis: Observer-based control for systems with slope-bounded nonlinearities.
• Daniel Mezei, codavised with Levente Tamas. Thesis: Active Perception for an Object-Sorting Robot.
• Ivo Grondman, coadvised with Robert Babuska at the Delft University of Technology, The Netherlands. Thesis: Online Model Learning Algorithms for Actor-Critic Control.
• Guoxi Feng, coadvised with Thierry-Marie Guerra at the Universite Polytechnique Hauts-de-France, France. Thesis: Mobility aid for the disabled using unknown input observers and reinforcement learning.
• Koppany Mathe, coadvised with Liviu Miclea. Thesis: Nonlinear Control for Commercial Drones in Autonomous Railway Maintenance.

Moreover, I am always on the lookout for motivated, capable students to work on a wide range of topics, from interesting applications to mobile aerial, underwater, and ground robots, through machine learning projects, to analytical research on control and optimization for the more mathematically inclined. Students should be ready to invest themselves fully into the project starting early on (for instance, in Bachelor year 3 or Master year 1); if you fit this profile, contact me for details.

Last update: Wednesday, 29 April 2026.