Milan, Italy ✱Machine Learning Engineer
I teach machines to see bodies heal, read symptoms, and predict cities in motion.
01 — Selected work
Post-stroke rehabilitation from a smartphone camera. OpenCV movement recognition plus a custom PyTorch model that gives patients real-time exercise feedback from visual data alone.
NLP classifier predicting 22 medical diagnoses from patient symptom descriptions (GretelAI dataset, 1,065 samples). Fine-tuned transformer models: BERT/ClinicalBERT (97% acc, F1=0.97) and Flan-T5 (94% acc), outperforming classical baselines.
Regression models to predict bike sharing demand from weather, time, and calendar features. Implemented preprocessing (scaling, one-hot encoding, PCA/LDA) and trained models including Linear/Ridge/Lasso/ElasticNet, Random Forest, and XGBoost.
02 — Experience
Sopra Steria
Leading development of Reviva, a post-stroke rehabilitation app: computer vision for real-time movement recognition, transformers that turn symptom descriptions into diagnoses.
University of Pavia / Milan
Conducted independent research on advanced machine learning and NLP applications. Developed multiple projects combining computer vision, transformers, and statistical models.
03 — Recognition
3rd
of 0
EIT Health i-Days
Madrid — 2025
Reviva, post-stroke rehabilitation with real-time camera feedback.
Winner
Biomedicine & AI
Bologna StartUpDays
Bologna — 2024
Track winner among national startup teams.
3rd
of 0
EIT Health i-Days
Italy — 2024
Represented Italy at the European Finals.
Also: Machine Learning Specialization (Andrew Ng, 2024) · Advanced Python for Data Science (University of Pavia, 2023)
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04 — About
The models above are the pitch: computer vision that watches stroke patients recover, transformers that sort symptoms into 22 diagnoses, ensembles that forecast bike demand across a city. Three international juries put them on podiums. The spec sheet has the rest.