Research at the edge of statistical physics, applied math and computer science

We use network and data science to study structure, dynamics and resilience of real-world systems


  • Multilayer analysis of structure and function, with focus on brain diseases such as Alzheimer's, Attention Deficit Hyperactivity Disorder, Autism and Schiozphrenia.

    Human Brain

  • Multilayer modeling and analysis of omics, with the aim of integrating systems for better understanding life and human diseases.

    Life and Disease

  • Big-data-driven multilayer modeling of complex dynamics to drive information awareness and contain the spreading of epidemics diseases.

    Computational Epidemiology

  • Multilayer analysis of online social activity and collective attention, computational modeling of individuals' behavior.

    Computational Social Science

  • Big-data-driven multilayer modeling of (smart) urban systems and human mobility.

    Transportation and Mobility

  • Multilayer risk analysis of integrated social, political, economical, technological and ecological systems, to support policy and decision-making.

    Global Risks and Policy

  • Blockchain-based solutions for the scientific environment, from peer-review system to replicability and reproducibility.

    Blockchain-based Applications

  • Development of advanced statistical and visualization tools for the analysis of data generated by complex systems.

    Multivariate Analysis and Viz

Theoretical Framework for Applied Research

Network Structure

Theoretical advance on the representation of complex networks for modeling empirical complex systems, identifying central/influential units and determine the underlying meso-scale organization.

Learn more »

Complex Dynamics

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.

Learn more »

Network Information

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.

Learn more »

Network Geometry

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.

Learn more »