Education

ETH Zürich, Zürich, Switzerland
MSc. Electrical Engineering and Information Technology
2019-2021
Specialization in Embedded Systems, Low-Power Design, Sensor systems, Machine Learning, TinyML. Additional courses in Communication Networks, Network and System Security and VLSI.

Polytechnic of Turin, Turin, Italy
BSc. Electronic Engineering
2016-2019
Fundamentals of Engineering, fundamentals of Circuits, Analog and Digital Electronics.
During the academic year 2018-2019 I joined the local student team PoliTOcean, developing an underwater robotic system to compete at the international MATE ROV competition. I developed, assembled, and tested parts of the control board of the system.

Liceo Salesiano Valsalice, Turin, Italy
Highschool Scientific Diploma
2011-2016

Work Experience

ETH Zürich - PBL, Scientific Assistant
2021-now

I currenly work as a Research Assistant at the Center for Project-Based Learning at ETH Zürich. Thanks to the multi-disciplinary nature of the Center, I developed a strong scientific approach to problem-solving, backed by literature research. I worked on several projects with different scope, such as

  • TinyML and Sensor Fusion: I fully developed several systems powered by TinyML and novel sensors for research purposes, covering audio applications (Voice Activity Detection), gesture recognition, eye-tracking, and motion classification.
  • Low-power Embedded and Bluetooth-LE: I designed prototypes for battery-operated devices, with constraints of dimension and energy budget. Most of the designs include sensing, real-time signal processing and Bluetooth-LE communication.
  • mmWave Radar: I investigated mm-Wave radar technology for biomedical applications (contactless respiration and heart rate monitoring, remote ECG), human-machine interfaces (gesture recognition), and autonomous robotics (perception in the autonomous racing field).

In parallel with the research, I supervised more that 10 student theses, outlining the scope of the thesis within the overarching research goals of the lab, organizing milestones and timeline, and providing the necessary support and knowledge, helping the students to bring forward their own research value while learning to handle complex projects.


Microtecnica S.r.l., Summer Intern
2015
Summer internship in the Engineering Area. I analysed data from public databases to evaluate the Mean Time Between Failure (MTBF) of some aircraft components to improve reliability.

Selected Projects

Wearable IMU and sEMG bracelet, Research Project
2023
I developed prototypes for a motion-tracking bracelet and for a sEMG-based gesture recognition bracelet. The design encompasses the entire stack, including hardware design (PCB), firmware, on-device signal processing, BLE communication and a data sink on a PC for visualization.

Wearable and Embedded Gesture Recognition with Novel Short-Range Radars, Master’s Thesis
2021
I developed a system for contactless gesture recognition based on novel low-power radars, in the form of a battery-operated earbud device. This project cut across the entire stack, starting from the dataset acquisition and machine learning modelling, to the PCB/hardware design, firmware development, signal processing, and Bluetooth-LE communication, ending with an Android app for a final demo.

Optical Flow for Drones on PULP, Semester Thesis
2020
During the Thesis, I ported the driver for an Optical Flow Sensor (PMW3901, PixArt) from the STM32 to a PULP chip. The sensor aids the stabilization of a Crazyflie drone and has been used in a PULP-based nano-UAV.

Federated Learning on PULP, Course Project
2020
I developed from scratch a CNN model, with support for both forward and backward propagation for learning. The implementation, written in C, was designed for efficient parallelization on an 8-core open-source hardware platform (PULP), and has served as a starting point for further development.

Deep Convolutional Networks on STM32, Course Project
2020
I developed and evaluated different techniques to port a Keras neural network on the STM32, focusing on the efficiency and accuracy tradeoff between a full-precision and a quantized version.

Computer Skills

Python, C, C++, Linux, Git, Tensorflow, Keras
Advanced
Bash, LaTeX, PyTorch, Rust
Intermediate
HTML, CSS, VHDL, Verilog, PHP
Basic

Languages

Italian, English
Proficient
German
Basic