Luca Furieri
Papers
1
Total Citations
1
H-Index
1
About
Luca Furieri is a control systems researcher whose work sits at the intersection of optimization, machine learning, and modern control theory. His research focuses on developing principled frameworks for designing high-performance, stable controllers for complex dynamical systems, with particular emphasis on bridging classical control theory with contemporary neural network architectures. A central contribution of his work is the development of parametrizations of all stable closed-loop responses, providing a rigorous theoretical foundation that extends to neural network-based control design for nonlinear systems. This line of research addresses one of the fundamental challenges in modern control: guaranteeing robust stability in output-feedback settings while preserving the expressive power needed for high performance in ℓp-stable discrete-time systems. By connecting deep learning tools with formal control-theoretic guarantees, Furieri's contributions open pathways for deploying learned controllers in safety-critical applications where stability certification is non-negotiable. Though his work is still gaining traction in the research community, the foundational nature of his contributions — unifying optimization-based and learning-based paradigms — positions him as an emerging voice in the next generation of control theorists tackling the challenges posed by increasingly complex autonomous and cyber-physical systems.
Research Focus
Key Achievements
Top Papers
- 1