About

About me

I am a Ph.D. graduate from 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 University of Genova. My research interests revolve around TinyML, Edge Intelligence, Embedded Computer Vision, Binary Neural Network, and Memory Optimization. I have a keen interest in developing efficient machine learning algorithms that can run on resource-constrained devices.

Team

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

Publications

We’ve authored numerous papers on TinyML. For more details, check out my Google Scholar profile

Software

As advocates of open-source development, we have released several frameworks that are aimed at making 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 autonomous edge learning and inferencing pipeline for resource-constrained embedded system,
CBin-NN is an inference engine for Binarized Neural Networks on resource-constrained devices.

Teaching

I am also 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 in the interior sensing team.