PhD Student in Information Engineering · University of Padova
Spiking neural networks, local learning, and adaptive computation
I work on spiking neural networks, especially local learning rules, temporal processing, and efficient neuromorphic computation.
Research interests
About
I am a PhD student at the Department of Information Engineering, University of Padova, working on spiking neural networks and local learning. In particular, I study network architectures and learning rules that rely on spikes, temporal information, and local plasticity rather than conventional end-to-end training alone.
My background is in physics and machine learning. More recently, my work has focused on reward-modulated learning, predictive processing, and the interaction between fast adaptation and longer-term memory. I am also interested in what changes when these models are designed for actual neuromorphic hardware.
Research directions
Deep spiking networks and local learning
I study how spiking neural networks can move beyond shallow pipelines through deeper architectures, structured connectivity, and learning rules such as STDP and reward-modulated plasticity.
Multi-timescale adaptation and memory
I am interested in systems that combine fast adaptation with slower consolidation, with the goal of improving continual learning, robustness, and online behavior under changing conditions.
Predictive and energy-aware intelligence
I explore connections between spiking computation, predictive processing, and adaptive control in resource-constrained systems where sensing, learning, and computation must be carefully regulated.
Selected projects
Human Activity Recognition with mmWave Radar
A project on activity recognition from mmWave radar data, combining sensing, signal processing, and machine learning on motion measurements collected across multiple subjects and environments.
Small-Footprint Keyword Spotting
A compact audio classification pipeline for keyword spotting based on convolutional neural networks, designed with lightweight inference and constrained deployment scenarios in mind.
Lab Workflow Automation
A lightweight automation project built to support everyday workflows, coordination, and small infrastructure tasks in a shared research environment.
Notes
I occasionally write about papers, experiments, and ideas related to my research.
Contact
The easiest way to reach me is by email. You can also find my code and technical work on GitHub.