CV

Machine Learning Intern

ML Cube

• Optimization of Multi Head Weighting machines using Reinforcement Learning.
• Engineered features from multi-sensor time-series data for process phase classification.
• Applied PCA, DTW, and statistical tests to identify critical sensors and patterns.
• Built pipelines for time-series decomposition, FFT, and feature extraction in Python.
• Contributed to the design of a Reinforcement Learning-based dynamic pricing and advertising algorithm for hotel rooms.
• Constructed full RL pipeline for hyperparameter selection and model evaluation using Optuna and MLFlow.

Summer Researcher

University of Science and Technology of China

• Extracted camera locations and 3D mesh from 360 head scan videos using COLMAP and FaceVerse.
• Implemented k radial diffusion algorithm to enhance mesh points with color information from COLMAP.
• Successfully applied 2DGS to the enhanced mesh points using PyTorch obtaining high-quality renders.
• Findings presented in a poster showcase.

Software Engineer (Backend) - Part Time

Digitiamo

• Implemented and maintained over 50 API routes across 20+ resources.
• Developed over 300 automated tests using Mocha, Chai and Sinon, increasing test coverage to over 90%.
• Refactored the relational database, reducing query times from minutes to seconds.
• Contributed to the introduction of Kafka, which completely eliminated TimeOutErrors between microservices.
• Collaborated in building AWS infrastructure using Terraform and Kubernetes accelerating product shipment.
• Contributed to the introduction of CI/CD pipelines, reducing the deployment process from 6 hours to just 1 hour.

Doctoral Student

ELLIS Doctoral Program

Continual Learning

• Working on Continual Learning under Claire Vernade and Marlos C. Machado.

Master of Science

Politecnico di Milano

Computer Engineering - Artificial Intelligence

• Associations: Polimi Data Scientists (Project Team Leader & Reading Group manager), Blackswan Quants Polimi.
• Exchange: ESIEE Paris (France), Erasmus+ program.
• PhD level courses: Reinforcement Learning.
• Thesis: A Reinforcement Learning Approach for Health-Behavioural Recommendations to Reduce Cancer Risk. Supervised by Prof. Francesco Trovò and Prof. Alberto Maria Metelli.

Bachelor of Science

Politecnico di Milano

Computer Engineering

• Member of the student representatives for the course of computer engineering.
• Exchange: VIA University College (Denmark), Erasmus+ program.

``Can you be my mum?'' Manipulating Social Robots in the Large Language Models Era

HRI 2025

A Reinforcement Learning Approach for Health-Behavioural Recommendations to Reduce Cancer Risk

ARLET@NeurIPS 2025 Workshop and WiML@NeurIPS 2025 Workshop

2nd place - Google Developer Student's Club AI Hack 2024

Among the top 5 teams of the fintech track over 150 selected students. Built an application for financial education powered with LLMs, presenting a working demo that could interpret the user’s personal finance situation and give improvement suggestions as well as learning resources.

First place - Assosoftware Hackathon

Built an explainable stock picker algorithm to help with the selection of stocks for client portfolios. Project featured in the final Assosoftware conference in Rome.

Mentee - Lead The Future

Among the few Italian students selected as mentees for LeadTheFuture, a leading STEM mentorship non-profit with an acceptance rate below 20%, offering one-on-one guidance from high-impact mentors at the world’s leading innovation hubs such as Silicon Valley and CERN.

2nd place - Google Developer Student's Club AI Hack 2025

Built an application for personalised text re-elaboration based on online learning.

PoliHUB Award Winner - University Startup Challenge 2025