Master's Degree in Data Science
Sapienza University of Rome
Expected Graduation Date: July 2025.
Sapienza University of Rome
Expected Graduation Date: July 2025.
Test of English as a Foreign Language (TOEFL iBT)
Issued November 2024. Report here.
Fully-funded 2 weeks summer school studying different topics on the frontier of the Physics of Complex Systems research, including: Inequality on Networks, Dynamical Systems, Neuroscience, Sports Modeling, among others.
Python project for the Social Networks & Online Markets exam for the MSc. in Data Science at the Sapienza University of Rome. The main purpose of the project was assessing the effect of link recommenders on emergent phenomena like popularity bias, the echo chamber effect and polarization on networks. This project is a reproduction and extension of the article: The Effect of People Recommenders on Echo Chambers and Polarization by Cinus et al. (2022)
Python project developed for the Advanced Machine Learning exam for the MSc. in Physics at the Sapienza University of Rome. The main purpose of the project was to recreate the results obtained in the article: Graph Coloring with Physics-Inspired Graph Neural Networks by Schuetz et al. (2022) by framing the Graph Coloring problem as a multi-class node classification problem and utilize an unsupervised training strategy based on the Statistical Physics Potts model.
R project developed for the final exam of the Statistics for Data Science course for the MSc. in Data Science at the Sapienza University of Rome. The main purpose of this project was to reproduce and extend some of the results presented by the published article: A Bayesian Approach to Predict Football Matches with Changed Home Advantage in Spectator-Free Matches after the COVID-19 Break by Lee et al. (2022) by using Bayesian Modeling and JAGS to assess the change in home advantage and other parameters in football after the COVID-19 pandemic.
Python project for the Algorithmic Methods of Data Science class for the MSc. in Data Science at the Sapienza University of Rome. The main purpose of the project was building a Recommendation Engine using Locally Sensitive Hashing and building a parallelized K-Means algorithm to cluster users based on their Netflix activity.