I am a computational physicist working at the intersection of network science and big data. My Ph.D. dissertation focused on networks, particularly on the interplay of the network structure and the dynamic of the diffusive processes on it. I developed simulation-based models and tools, employing them to study percolation of diffusive phenomena on networks, specifically problems pertaining to public health and to electrical infrastructures.

I began my career in quantum condensed matter at Scarola Group, where we used advances in computational techniques to connect quantum models and experiments on optical lattices. My interest in applied problem solving led me toward studying complex networks (Joined NDSSL in 2011). My research focused on developing models and tools to improve our understanding of complex networks (see publications).

Recently, I have also become interested in the Science of Success field (I'm one of the organizers of the "quantifying success" satellite session at NetSci 2017). My current project focuses on the interaction of social network and scientific merit on the perceived success of scientists. Science is a social endeavor and therefore the perceived value of a work is deeply intertwined with the scientist’s own reputation and the prominence of their home institution and their educational history. Indeed, for a paper (finding), an individual (career), or an institution to be successful, the community at large must agree that it is worthy of follow-up and this consensus has detectable signatures in the scientific literature.

In my research, I combine tools of statistical physics, computer science, and machine learning to unravel the contributing factors and the role they play in the impact of a scientist. I will take advantage of recent concurrent advances in network science and the availability of massive datasets on scientific production, to disentangle the inherent scientific merit from the potential interferences induced by the social nature of the scientific enterprise.