I study integrated real-world Complex Systems (biological, socio-technical and engineering networks) and their complex dynamics, to understand how multiplexity and interdependencies lead to emergent collective phenomena and resilience to perturbations.
Bio for details Highlights for latest publications, events and funding.
Since November 2022 I curate the Complexity Thoughts newsletter, where you can subscribe for free by clicking here. Find me on Mastodon and Twitter.
With my Lab, I focus on some fields of interests that can be summarized in the following three pillars:
How the statistical physics of information pathways can be used to understand the emergence of complex features in networks
The most updated review on the physics of complex networks robustness and resilience
The most updated review on the physics of multilayer networks, with a strong focus on real-world systems
Between June and July we obtained funding by Italian MUR with a PRIN (co-PI), a PRIN PNRR (PI) and the prestigious FIS (PI), for a total of 1.3 million euros.
Statistical inference links data and theory in network science
We obtained a 1 million dollars funding by the Human Frontier Science Program, in collaboration with U. Ben Gurion (Israel) and U. Liverpool (UK)
Emergent phenomena in complex physical and socio-technical systems: from cells to societies
A Cambridge element from the Complex Multilayer Networks Lab.
Analysis & visualization with muxViz in R.
Machine learning dismantling and early-warning signals of disintegration
Understanding the latent geometry of empirical complex systems, topologically and functionally.
Modeling of epidemics spreading and social dynamics to understand the risk of outbreaks in Turkey.
Theoretical advance on the representation of complex networks for modeling empirical complex systems, identifying central/influential units and determine the underlying meso-scale organization.
Single and coupled dynamics on multilayer networks for modeling information/awareness propagation, complex contagion, epidemics spreading, consensus mechanisms. Our goal is to better understand robustness, resilience and emergence of collective phenomena in complex networked systems.
Information theory is intimately realted to statistical physics, playing a key role in data science and a variety of applications. We develop theoretical and analytical tools to quantify how complex networks produce and process information, to reduce their dimensionality.
Network geometry is rapidly gaining attention for providing a suitable framework for the analysis of interacting systems. We focus on the application of network diffusion maps to better understand the dynamics of spreading processes and to provide coarse-grained representation of networkd systems.