About

About me

I am a Ph.D. graduate of the Joint Doctoral Programme in Interactive and Cognitive Environments (JD ICE), organized by the Centre for Intelligent Sensing at Queen Mary University of London and the Department of Electrical, Electronic, Telecommunications Engineering and Naval Architecture at the University of Genova. My research focuses on TinyML, edge intelligence, embedded computer vision, binary neural networks, and memory optimization. I am passionate about designing efficient machine learning algorithms that run on resource‑constrained devices.

Team

During my Ph.D., I had the opportunity to work with outstanding researchers and professors, including Francesco Bellotti, Riccardo Berta, and Joseph Doyle.

Publications

I have co‑authored numerous papers on TinyML. For more details, see my Google Scholar profile.

Software

As advocates of open‑source development, we have released several frameworks that make embedded machine learning more accessible. These include:

ELM (Edge Learning Machine) is an environment for embedded machine and deep learning:
- Desk-LM is a Python module for training machine learning models.
- Micro-LM is a plain‑C inference library for machine learning on edge devices.
Autonomous-Edge-Pipeline is a self‑learning pipeline for autonomous edge learning and inference on resource‑constrained embedded systems.
CBin-NN is an inference engine for binarized neural networks on resource‑constrained devices.

Teaching

I am passionate about teaching and enjoy sharing my knowledge with others. I have been a teaching assistant at Queen Mary University of London for:

Big Data Processing (ECS640U) - Fall’21
Machine Learning (ECS708P) - Fall’21

Current Position

Embedded Machine Learning Engineer at APTIV, working in the interior sensing field on driver monitoring systems and cockpit monitoring systems.