About us
Training a new generation of scientists in complex systems
Complexity Next Gen is a non-profit organization founded in 2026 in Torino, Italy, dedicated to training the next generation of researchers in complex systems.
It focuses on early-career scientists and young researchers, promoting innovative, hands-on, and interdisciplinary forms of scientific training.
Through immersive events and experimental formats—such as intensive workshops and research sprints—the association exposes participants to the full scientific process, from collaboration and problem framing to communication, leadership and article publication.
Complexity Next Gen aims to complement traditional academic paths by fostering creativity, openness, and cross-disciplinary thinking at the earliest stages of a research career.
Governance
EUGENIO VALDANO – President
Eugenio is a tenured researcher at the French National Institute of Health and Medical Research (INSERM). He is part of the Sorbonne Pubic Health Institute (SPH), attached to INSERM, in Paris, France. Eugenio is an infectious disease epidemiologist with a background in theoretical physics.
ALBERTO ANTONIONI – Vice President
Alberto is an associate professor at Universidad Carlos III de Madrid (UC3M), Spain. He is currently a member of the Interdisciplinary Group of Complex Systems (GISC), secretary of the Spanish Chapter of the Complex Systems Society, and Chair of the Conference of Complex Systems Steering Committee. He works at the intersection of game theory, experimental economics, and complex systems, with a background in mathematics and computer science.
IACOPO IACOPINI – Secretary
Iacopo is an Associate Professor in the Network Science Institute at Northeastern University London. His research interests are in the area of complex networks and computational social sciences, with a focus on behavioural contagion, discovery processes, and group dynamics.
MICHELE RE FIORENTIN – Treasurer
Michele is Assistant Professor at Politecnico di Torino. His research primarily focuses on matter physics, first-principles simulations of materials and on the investigation of complex properties through the application of machine-learning techniques and the development of machine-learning based interatomic potentials.
Collaborators
LORENZO BETTI
Lorenzo Betti is a Postdoctoral Researcher at the Department of Network and Data Science at Central European University, Vienna. His research focuses on collective behavior and group dynamics, investigating how individual interactions give rise to emergent forms of organization in human and animal systems using network science and data-driven methods.
ALESSANDRO GALEAZZI
Alessandro Galeazzi is a Tenure Track Assistant Professor at the Department of Mathematics, University of Padova. His research interests are in the area of computational social science and network science, with a focus on echo chambers, political polarization, misinformation, algorithmic auditing, and the evolution of cultural artifacts and the online information ecosystems.
BERNÉ NORTIER
Berné is a doctoral student at the University of St. Andrews, Scotland. She is interested in how complex systems which display collective dynamics and group behaviour are constructed and knit together. She has a special interest in latent space descriptions of such systems, specifically network geometry methods and how they observed features of the network. When she has free time, she thinks about sheaves and how to use them as tools in complex systems studies.
MADDALENA TORRICELLI
Maddalena Torricelli is a postdoctoral researcher at Northeastern University specializing in sports analytics. With a background in complex systems physics and computational social sciences, she holds a PhD in Data Science. Her research applies network science, data analytics, and complex systems methods to sports, focusing on team dynamics, performance analysis, and temporal patterns in gameplay. She is particularly interested in tactical dynamics, team strategies, and performance patterns in sports, leveraging data-driven approaches to improve decision-making and athlete performance.