I am a data/network scientist currently working in the Mathematics and Complex Systems group at ISI Foundation in Turin, Italy. I would summarize my research interests simply by using the title of my Ph.D. Thesis: Networks, Epidemics and Collective Behavior: from Physics to Data Science.

I like all the aspects that have to do with data, from obtention to visualization. As such, it is hard for me to focus on one single field. Nevertheless, I would say that because of my physics background, I am particularly interested in unraveling the mechanisms that lead to the data. That is, rather than simply focusing on finding correlations and making uninformed predictions, my aim is to understand the dynamical process that leads to it.

I also love software engineering, and that is why I am studying for a bachelor’s degree in computer engineering in my free time. I am still doubting whether shifting my career towards this, or keep working with data. We will see!

On the left side of this site, you will find my contact information if you want to have a chat. In Publications you can check my works. I would also suggest you to visit the Resources section, it is a bit brief for the moment, but I hope to extend it soon.

Selected publications

Location of COVID-19 infections

Here we integrate anonymized, geolocalized mobility data with census and demographic data to build a detailed agent-based model of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) transmission. Our results show that places are not dangerous on their own; instead, transmission risk is a combination of both the characteristics of the place and the behavior of individuals who visit it.

Data-driven contact structures

Current epidemic models still lack a faithful representation of all possible heterogeneities and features that can be extracted from data. Here, we bridge a current gap in the mathematical modeling of infectious diseases and develop a framework that allows to account simultaneously for both the connectivity of individuals and the age-structure of the population.

The dynamics of Twitch Plays Pokémon

Here, we study the dynamics of a crowd controlled game (Twitch Plays Pokémon), in which nearly a million players participated during more than two weeks. Unlike other online games, in this event all the players controlled exactly the same character and thus it represents an exceptional example of a collective mind working to achieve a certain goal.