Luca Furieri — SNSF Ambizione Fellow

Incoming (June 2025) Associate Professor at the University of Oxford
and Tutorial Fellow at St Hugh's College

Swiss National Science Foundation (SNSF) Ambizione Fellow at EPFL
ME C2 399 (Bâtiment ME), Station 9, 1015, Lausanne

Prospective PhD students: I will be joining the Control Group at the University of Oxford as an Associate Professor in June 2025. I am currently seeking for exceptional students with a strong background in mathematics, theory, and computation, to join in October 2025. If you have a strong interest in optimization and control, I encourage you to apply.

Drop me an email (luca.furieri@epfl.ch) with your CV, grade transcripts, and a brief overview of your interest in joining my group.

Brief Biography

I am an SNSF Ambizione Fellow at EPF Lausanne since January 2023. I will be joining the Department of Engineering sciences at the University of Oxford as an Associate Professor in June 2025. My research focuses on learning and optimal control for distributed decision-making and large-scale safety critical applications.

Previously, I have been a Postdoctoral researcher at the Automatic Control Laboratory, EPFL, working in Giancarlo Ferrari Trecate's group, DECODE. In September 2020, I have received the Ph.D. degree in Control and Optimization from ETH - Zurich in the Automatic Control Laboratory (IfA) under the supervision of Prof. Maryam Kamgarpour. I have received the Bachelor and Master degrees in Automation Engineering from the University of Bologna, in 2014 and 2016 respectively.

I have received the SNSF Ambizione career grant in 2022. My papers have been awarded the IEEE Transactions on Control of Network Systems Best Paper Award in 2022, the European Control Conference Best Paper Award (finalist) in 2019, and the American Control Conference O. Hugo Schuck Best Paper Award in 2018.

Research

Below you will find a few slides that describe my recent research work.

Brief overview of selected contributions

DNN control, distributed PH control, regret-optimal control

Learning to optimize with convergence guarantees

Deep Neural Network Control

Recent updates

09/2024: Our paper On the guarantees of minimizing regret in receding horizon has been published in the IEEE Transactions on Automatic Control.
08/2024: Our paper Learning to boost the performance of stable nonlinear systems has been accepted for publication in the IEEE Open Journal of Control Systems.
06/2024: Attended the European Control Conference (ECC) in Stockholm!
- Organized The Workshop Neural Networks in Control.
- Paper Closing the gap to quadratic invariance: a regret minimization approach to optimal distributed control.
- Paper Regret optimal control for uncertain stochastic systems (also published in the European Journal of Control).
- Paper Unconstrained learning of networked nonlinear systems via free parametrization of stable interconnected operators.
06/2024: I gave a talk in the Kolloquium Technische Kybernetik of the University of Stuttgart on Learning to optimize with convergence guarantees (slides available here) and visited the Institute of Systems Theory and Automatic Control hosted by Prof. Andrea Iannelli.
05/2024: Our paper Learning to optimize with convergence guarantees using nonlinear system theory has been accepted for publication in the IEEE Control Systems Letters (L-CSS).