Senior Research Scientist

ISI Foundation


I am a Senior Research Scientist at ISI Foundation working on topological approaches to complex networks and their underlying geometry, with special attention to the topology of brain structure and dynamics.


  • Topology and Predictability of Complex Systems
  • Computational Neuroscience
  • Cognitive Control


  • PhD in Complex Networks, 2012

    Imperial College London

  • MSc in Theoretical Physics, 2008

    University of Pisa

  • BSc in Physics, 2005

    University of Pisa

News & Highlights


10.3.21 I’m giving a talk at the “Virtual Seminars in Complexity” series this friday. Details here. [Recorded talk is now available here]

7.3.21 Quite a few news recently! Starting Feb 2021 for a year I’ll be a Guest Scholar at IMT Lucca visiting the NETWORKS Unit there!

18.2.21 Our paper on limits of parallel capacity is finally out on Nature Physics! You can find the paper here, and a little blurb explaining it here.

8.2.21 New paper “Simplicial and Topological Descriptions of Human Brain Dynamics” out on Network Neuroscience!


3.6.20 New BIG review on higher-order interactions out!! You can find it here. A nice intro to the paper is instead given on Iacopo Iacopini’s page here).

Recent Publications

Topological limits to the parallel processing capability of network architectures

The ability to learn new tasks and generalize performance to others is one of the most remarkable characteristics of the human brain …

Simplicial and Topological Descriptions of Human Brain Dynamics

Time-varying functional connectivity interprets brain function as time-varying patterns of coordinating brain activity. While many questions remain regarding how brain function emerges from multi-regional interactions, advances in the mathematics of Topological Data Analysis (TDA) may provide new insights. One tool from TDA, “persistent homology”, observes the occurrence and persistence of n-dimensional holes in a sequence of simplicial complexes extracted from a weighted graph. In the present study, we compare the use of persistent homology versus more traditional metrics at the task of segmenting brain states that differ across experimental conditions. We find that the structures identified by persistent homology more accurately segment the stimuli, more accurately segment high versus low performance levels under common stimuli, and generalize better across volunteers.

Networks beyond pairwise interactions: structure and dynamics

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.

Homological Scaffold via Minimal Homology Bases

The homological scaffold leverages persistent homology to construct a topologically sound summary of a weighted network. However, its …

Social contagion models on hypergraphs

Here we study the dynamics of social contagion on hypergraphs. We develop an analytical framework and provide numerical results for arbitrary hypergraphs, which we also support with Monte Carlo simulations. We show that the model has a vast parameter space, with first- and second-order transitions, bistability, and hysteresis.


ISI Team