Simona Olmi

Address: via Madonna del Piano 10, 50019 Sesto Fiorentino

Short Bio

Simona Olmi obtained her MSc (cum laudae) in Theoretical Physics at the University of Florence in 2009 with a dissertation on the emergence of collective oscillations in massively connected neural networks. In 2013 she obtained her PhD in Nonlinear Dynamics and Complex Systems at the University of Florence with a thesis on pulse coupled neural networks, focused on the role played by the topology in the emergence of collective states displaying erratic behaviours at the macroscopic and microscopic level.

Afterwards she worked at the Istituto dei Sistemi Complessi (ISC-CNR) in Sesto Fiorentino (Italy),
at the Institut de Neurosciences des Systemes (INS) in Marseille (France), at the Weierstrass Institute for Applied Analysis and Stochastics (Berlin, Germany) and at the Institut für Theoretische Physik (Technische Universität Berlin – Berlin, Germany). She got a Starting Research Position at Inria Sophia Antipolis Méditerranée Research Centre (France) and, from 2019, she is permanent staff of the ISC-CNR-Institute, where she is senior researcher.

She has been a principal investigator of the French-German collaboration project PROCOPE 2019 – Partenariat Hubert (2019-2020) and of the PhD program ‘Next Generation Neural Mass Models’ founded by SAM Inria Sophia Antipolis Research Centre (2018-2021), a member of the Advanced Study Group, founded by the Max-Planck Gesellschaft at the Max-Planck-Institut für Physik Komplexer Systemen (MPIPKS) in Dresden and of the project CRISIS-LAB (PNR 2011-2013) on the development of a laboratory and an integrated platform system for the collection and analysis of data on crisis prevention and management in complex economic and social systems.

She led a grant EBRAINS Research Infrastructure Voucher Call 2020 in the Human Brain Project framework and is part of the Research Project “National Center for HPC, Big Data and Quantum Computing – HPC” founded by the European Union – NextGenerationEU.

The research activity of Dr Simona Olmi ranges from statistical physics and nonlinear dynamics to (computational) neuroscience and complex networks; her activity has definitely an interdisciplinary approach, since she applies techniques and methods borrowed from statistical physics and nonlinear dynamics to characterize complex systems, like biological systems or power grid systems in terms of their dynamics and stability.

Dr. Olmi has published more than 40 peer-reviewd articles which appeared in the most prestigious international peer-reviewed Journals (Hirsch index h=23, google scholar) and she is editor of Physica D: Nonlinear Phenomena.

She has organized 20 international conferences, among which the conference “Half a Century of the Kuramoto Model – WE Heraeus Seminars 2026”, which has a special focus on the application of nonlinear dynamics and complex systems on power grid analysis.

Research interests

The science of complexity is a new and extremely trans-disciplinary field of research. Complex systems are those composed of a large number of interacting elements, so that the collective behaviour of those elements goes far beyond the simple sum of the individual behaviours. Initially the concept of complex systems was mainly associated with the temporal evolution of systems made up of many interacting units each characterized by highly nonlinear dynamics.

In the last decade the interest has moved towards an even more intriguing subject: the emergence of nontrivial collective dynamics in networks composed of elements whose evolution is extremely simple, like oscillators with periodic dynamics. My research investigation is focused on interacting sub-populations of oscillators, with the goal of characterizing the dynamics both at the macroscopic (collective) level, as well as at the level of each sub-population (mesoscopic level). Two apparently quite distant research fields where the mesoscopic evolution of sub-networks is extremely relevant are neural circuits and electrical power-grids.

Theoretical methods and techniques developed for coupled oscillators (exact reduction methodologies) can be profitably applied in the context of neural networks and power-grids, since these systems can be mathematically modelized as networks of coupled phase oscillators.