I am an Associate Professor of Engineering Science at the University of Oxford and a Tutorial Fellow at St Hugh’s College.
My group works at the interface of control, optimization, and machine learning. We study how large-scale interconnected dynamical systems can be controlled, adapted, and optimized under structural, informational, and safety constraints, with a particular focus on modular and distributed control, learning-enabled control with guarantees, and control-inspired approaches to algorithm design.
Before joining Oxford, I was a Principal Investigator and postdoctoral researcher at EPFL. I received my PhD in 2020 from ETH Zurich (Automatic Control Laboratory, IfA). Selected recognitions include the SNSF Ambizione career grant, the TCNS Best Paper Award, and the ACC O. Hugo Schuck Award.
Recent updates
- 11/2025 PhD applications for the DPhil in Engineering Science are open. Deadline: 2 December 2025, 12:00 (UK) for entry in October 2026. If you are interested in working with me, contact me as soon as possible to discuss. Formal applications must be submitted via the Oxford admissions page: DPhil in Engineering Science (Oxford).
- 10/2025 🎉 Excited to welcome our new PhD students - John Cao, Junan Lin, and Yuhang Ye — to the group! You can find their profiles on the People page.
- 09/2025 Excited to share that our paper, “Learning to optimize over linearly convergent algorithms: gotta characterize 'em all” (Andrea Martin, Ian R. Manchester, Luca Furieri), has been accepted for an oral presentation at the ScaleOPT workshop, NeurIPS 2025. Preprint available at arXiv:2508.00775.
- 09/2025 I have given a talk at the AI and Data Talk Series Webinar of the ACE Network, where I discussed how ideas from neural network control and learning-based optimization can benefit each other. See the LinkedIn post.
- 08/2025 Excited to share our latest preprint on learning to optimize: Learning to optimize with guarantees: a complete characterization of linearly convergent algorithms, by Andrea Martin, Ian R. Manchester and Luca Furieri.
- 07/2025
CDC 2025 results are out:
MAD: A Magnitude And Direction Policy Parametrization for Stability Constrained Reinforcement Learning was accepted for presentation.
Do not miss a line-up of amazing speakers at the invited session on "Control Theory for Algorithm Analysis and Design". Organized by Andrea Martin, Guido Carnevale, Nicola Bastianello and myself.
- 06/2025 Invited talk on learning to optimize with convergence guarantees at the ECC 2025 workshop “Systems Theory of Optimization, Learning, and Control Algorithms”.
- 06/2025 Joined the University of Oxford as an Associate Professor of Engineering Sciences.
- 04/2025 New preprint MAD: A Magnitude And Direction Policy Parametrization for Stability Constrained Reinforcement Learning on arXiv.
- 02/2025 Had a great time visiting Prof. Yang Zheng and his group at UC San Diego. I shared recent work on learning-based control and optimization. LinkedIn post.
- 09/2024 Paper On the guarantees of minimizing regret in receding horizon accepted in the IEEE Transactions on Automatic Control.
- 08/2024 Paper Learning to boost the performance of stable nonlinear systems accepted 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 Regret optimal control for uncertain stochastic systems (also published in the European Journal of Control).
- 06/2024 Invited talk at University of Stuttgart (Kolloquium Technische Kybernetik) here. I also had the pleasure to visit 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 was accepted