I am a physicist with a strong interest in applied sciences. My research deals with Complex Networks, Data Science and their application to the study and modeling of social and urban systems.
I am a PhD Candidate at Queen Mary University of London, where I work within the Complex Systems and Networks Research Group under the supervision of Vito Latora (QMUL). I am also working at the Urban Dynamics Lab of the Centre for Advanced Spatial Analysis (CASA), UCL, under the supervison of Elsa Arcaute. I collaborate with the Dynamical Systems and Statistical Physics Group (QMUL) as part of the LoBaNet project.
I spent part of my PhD at The Alan Turing Institute (London) as part of the enrichment doctoral scheme. Previously, I worked at the ISI Foundation in Turin within the Data Science and Mathematics & Foundation of Complex Systems groups. Before, I was Data Science Intern at UN Universal Postal Union in Bern.
PhD Candidate in Mathematics
Queen Mary University of London
MSc in Physics of Complex Systems, 2015
University of Turin
BSc in Physics, 2013
University of Bologna
The complexity of many biological, social and technological systems stems from the richness of the interactions among their units. Over the past decades, a great variety of complex systems has been successfully described as networks whose interacting pairs of nodes are connected by links. Yet, in face-to-face human communication, chemical reactions and ecological systems, interactions can occur in groups of three or more nodes and cannot be simply described just in terms of simple dyads. Until recently, little attention has been devoted to the higher-order architecture of real complex systems. However, a mounting body of evidence is showing that taking the higher-order structure of these systems into account can greatly enhance our modeling capacities and help us to understand and predict their emerging dynamical behaviors. Here, we present a complete overview of the emerging field of networks beyond pairwise interactions. We first discuss the methods to represent higher-order interactions and give a unified presentation of the different frameworks used to describe higher-order systems, highlighting the links between the existing concepts and representations. We review both the measures designed to characterize the structure of these systems, and the models proposed in the literature to generate synthetic structures, such as random and growing simplicial complexes, bipartite graphs and hypergraphs. We then introduce and discuss the rapidly growing research on higher-order dynamical systems and on dynamical topology. We focus on novel emergent phenomena characterizing landmark dynamical processes, such as diffusion, spreading, synchronization and games, when extended beyond pairwise interactions. We elucidate the relations between higher- order topology and dynamical properties, and conclude with a summary of empirical applications, providing an outlook on current modeling and conceptual frontiers.
Complex networks have been successfully used to describe the spread of diseases in populations of interacting individuals. Conversely, pairwise interactions are often not enough to characterize social contagion processes such as opinion formation or the adoption of novelties, where complex mechanisms of influence and reinforcement are at work. Here we introduce a higher-order model of social contagion in which a social system is represented by a simplicial complex and contagion can occur through interactions in groups of different sizes. Numerical simulations of the model on both empirical and synthetic simplicial complexes highlight the emergence of novel phenomena such as a discontinuous transition induced by higher-order interactions. We show analytically that the transition is discontinuous and that a bistable region appears where healthy and endemic states co-exist. Our results help explain why critical masses are required to initiate social changes and contribute to the understanding of higher-order interactions in complex systems.
We introduce a model for the emergence of innovations, in which cognitive processes are described as random walks on the network of links among ideas or concepts, and an innovation corresponds to the first visit of a node. The transition matrix of the random walk depends on the network weights, while in turn the weight of an edge is reinforced by the passage of a walker. The presence of the network naturally accounts for the mechanism of the adjacent possible, and the model reproduces both the rate at which novelties emerge and the correlations among them observed empirically. We show this by using synthetic networks and by studying real data sets on the growth of knowledge in different scientific disciplines. Edge-reinforced random walks on complex topologies offer a new modeling framework for the dynamics of correlated novelties and another example of co-evolution of processes and networks.
I am currenly Visiting Lecturer at City, University of London, for the course:
I’ve been teaching Network Science at the School of Electronic Engineering & Computer Science (QMUL):
I’ve been Teaching Assistant for the following courses at QMUL:
I’ve been a reviewer for the journals:
Chaos, EPJ Data Science, Physical Review E, Scientific Reports, Europhysics Letters, Frontiers in Physics, Chaos Solitons & Fractals, Mathematical and Computer Modelling of Dynamical Systems, Online Social Networks and Media.
Ghent University, BE.
University of Cambridge, UK.
National Grid, Wokingham, UK.
UPU, Bern, CH.
UPU, Bern, CH.