I’m a research engineer at Honda Research Institute Europe in Offenbach and a PhD candidate in electronics engineering at Sapienza University of Rome, defending in October 2026.
My work is deep reinforcement learning for sequential decision making under uncertainty: continuous and discrete action spaces, multi-objective and constrained formulations, and multi-agent settings. In the PhD I applied it to battery dispatch and real-time energy management for renewable energy communities, with a particular interest in policies that account for battery degradation rather than optimising cost alone. At Honda I work on multi-agent coordination under shared resource constraints, currently fair electric-vehicle charging under a shared power budget, where an explicit fairness constraint replaces hand-tuned reward weights. In between, as a guest researcher at TU Delft, I built a reinforcement learning environment for the long-term energy transition of the Netherlands at national scale.
Before energy systems I worked in computer vision and robotics at EPFL’s VITA Lab: deep stereo networks for depth estimation on unrectified 360-degree images, a new lidar-and-camera dataset, and a real-time person detection and tracking pipeline running on a Loomo mobile robot.
I write mostly in Python and PyTorch, with C/C++, MATLAB/Simulink and ROS where the hardware needs it.
See the CV, publications and projects. I’m available from November 2026, and LinkedIn is the best place to reach me.