NetSI London

Iacopo Iacopini is an Assistant Professor in the Network Science Institute at Northeastern University London. His research interests are in the area of complex networks and computational social science, with a focus on adoption processes, seeding/targeting strategies, and group and team dynamics. In parallel to his core research on human behaviour, he is also working on urban networks and animal social networks.

Iacopo combines theoretical developments with empirical analyses to study the role of social interactions in the dynamics of behavioural contagion, group formation and coordination, norm emergence, discovery, and innovation. His major contributions include studies on the emergence of novelties from random processes of network exploration, and how social interactions can affect their pace of discovery. Going beyond pairwise networks, Iacopo investigated the impact of group interactions on social dynamics, showing how higher-order approaches can lead to critical mass effects, sustain social contagion, and help minorities of committed individuals to overturn social conventions. He co-authored a major review and a perspective article on higher-order networks. For his early-career contributions to complexity science, he received the Emerging Researcher Award from the Complex System Society.

A physicist by training, Iacopo holds a PhD in Mathematics from Queen Mary University of London, where he worked across the Complex Systems and Networks and the Dynamical Systems and Statistical Physics groups. Prior to joining Northeastern London, he was awarded the JSMF Postdoctoral Fellowship by the James S. McDonnell Foundation, hosted at the Department of Network and Data Science at Central European University. He has held research positions at the Centre de Physique Théorique of Aix-Marseille University, at the Urban Dynamics Lab of the Centre for Advanced Spatial Analysis at University College London, and at The Alan Turing Institute --the UK national institute for Data Science and Artificial Intelligence. Previously, he worked at the ISI Foundation and at the United Nations - Universal Postal Union as a Data Scientist.

Riccardo Di Clemente is an Associate Professor at Northeastern University London, Core Faculty at NetSI Northeastern Univesity, Alan Turing Fellow of The Alan Turing Institute, and Visiting Professor at Sony Computer Science Lab Paris.

Riccardo obtained his undergraduate B.Sc. & M.Sc. degrees in physics at Sapienza University of Rome. He received his Ph.D. Europaeus in Economics (at IMTLucca, Italy with visiting Institution INET@Oxford) where he applied Complex Systems tools to Economics and Social system. Afterward, he completed his postdoctoral research at the Institute of Complex Systems in Rome and then at Civil and Environmental Engineering department of MIT in Boston. He has been awarded the Newton International Fellowship of the Royal Society in applied mathematics, he was hosted by the Centre for Advance Spatial Analysis (CASA) at University College London UCL. Before joining Northeastern University London, Riccardo was a lecturer in Data Science at the Computer Science Department of the University of Exeter.

Riccardo has been a consultant for the World Bank, he led projects in collaboration with companies and ONG (Bill & Melinda Gates Foundation, and Data2x) to develop fine-tuned data-driven solutions for policymaking in developing countries.

Riccardo is leading a multidisciplinary research group at Complex Connection Lab at Northeastern University. Riccardo's team develops innovative mathematical frameworks to analyze and model the complex social connections that govern human behavior and interactions within cities and online. Riccardo's research utilizes network theory, complex systems computational social science, and machine learning methods to investigate the digital footprints left by individuals in their daily routines.

Riccardo's research questions are built in close relationships with data providers and institutions. Using mobile phone data we decoupled the changes in human mobility across the UK during the Covid-19 health crisis; provided data-driven approaches for spatial planning, poverty reduction, and disaster resilience in developing countries such as Nigeria and Mexico. Leveraging Credit Card Data we identify socio-economic lifestyles within cities in developing countries and define gender economic and spatial routines in cities.

Currently, Riccardo's Lab is developing new Complex Systems and Machine Learning models to capture digital human traces to understand urban segregation. Additionally, they are studying the structure of social network conversations, to observe how major events affect society, conversation patterns, and users' interactions with respect to unperturbed discussion patterns.

Marilyn is a Postdoctoral Research Assistant in the Network Science Institute at Northeastern University London. Her research interests extend to several clinical challenges, including health, life expectancy, neurodegenerative diseases, and psychiatric disorders.

Marilyn is a mathematician with a dissertation applied to computational neuroscience. She obtained a double Ph.D. in Biophysics and Computational Biology at the University of Valparaíso in Chile and Biomedical Research at the Basque Country University in Spain. Continuing her academic journey, Marilyn worked as a postdoctoral researcher at the University of Nottingham. During her research, she has characterized and modeled the brain pattern organization using high-order interdependencies analysis in fMRI, distinguishing redundancy- and synergy-dominated interactions that play crucial roles in neural dynamics.

Giovanni Petri is a Professor at the Network Science Institute, Northeastern University London. With a broad background in theoretical physics and applied mathematics, he has successfully applied his expertise to address various research areas, including network science and higher-order systems, dynamical processes, with a particular focus on neuroimaging, computational neuroscience, and, more recently, artificial intelligence.

Alongside these core topics, his research also encompasses the study of group dynamics in social systems, novel methods for parsimonious feature selection, and information-theoretic limits to predicting complex systems. He has collaborated with researchers from diverse disciplines, including mathematicians, physiologists, psychologists, network neuroscientists, epidemiologists, and computer scientists.

Giovanni Petri has authored over 50 peer-reviewed articles in high-impact interdisciplinary journals such as Science, Nature Physics, Physical Review Letters, Nature Communications, Neuroimage, and the Journal of Neuroscience. Major contributions include a review and a perspective on higher-order systems, the topological functional structure of altered brain states,   a temporal model of simplicial interactions to describe the effects of group interactions in social spreading processes, a graph-theoretic approach to cognitive control to multitasking capacity, and the discovery of the evolutionarily conserved role of oxytocin for social contagion.   He guest-edited a special issue of "Network Neuroscience" dedicated to topological applications in brain networks (in collaboration with Prof. M. Kringelbach and Dr. Paul Expert) and co-edited the book "Higher-order Systems" (with Prof. F. Battiston) for the Springer Nature series "Understanding Complex Systems".  

Giovanni Petri obtained his PhD from Imperial College London in 2012 studying the mathematics of complex networks. He then joined ISI Foundation as a postdoctoral researcher working on the topological structure of complex systems.  Since 2016, Giovanni Petri has led his own lab, initially as a Senior Research Scientist at ISI Foundation and then as a Principal Researcher at CENTAI Institute.

He mentored and supervised 6 postdocs, 2 PhD students, 2 junior researchers, and 13 MSc and BSc students. Additionally, he leads a working group focused on the topology of learning systems, attracting participants from various research labs and institutions. To fund his research, he has secured both large grants, such as the 3-year ADnD project (Compagnia San Paolo) and the 5-year grant from Project CETI, as well as smaller grants targeting specific objectives.

Beyond his research, Giovanni Petri is committed to open reproducible science, scientific outreach, and public engagement. Together with his group and collaborators, he has developed and distributed a set of open-source tools for topological data analysis (TDA) and modeling (Holes, SCM, DyNeuSR, XGI), aiming to lower the entry barrier to TDA and network inference for scientists from non-mathematical or non-computational disciplines.He has organized multiple workshops, satellite meetings, and conferences to bridge disciplinary gaps and foster collaboration. This includes conducting a TDA crash course at the Applied Machine Learning Days 2019, organizing a series of satellite meetings (TopoNets) at both the NetSci conference and Conference on Complex Systems since 2014, and co-organizing the annual CCS Warm Up school, aimed at young researchers in complex systems, which is still ongoing.

In 2018, he served as a co-organizer of "ATMCS7: Conference on Algebraic Topology: Methods, Computation, and Science".
To enhance public awareness and build communities around scientific topics, he initiated initiatives such as Databeers Torino, which brings together individuals interested in data science through engaging talks and networking events. He has actively participated in other outreach initiatives, including Pint for Science."

István Z. Kiss is a Professor of Applied Mathematics. His research is at the interface of network sciences, dynamical systems and stochastic processes. In particular, he focuses on dynamical processes on static and dynamic networks, using mainly epidemic models. He is working on developing theoretical models that capture complexities arising in real networks, such as heterogeneity in the characteristics, behaviour and interaction of individuals, as well as higher-order network structure. He contributed significantly to: (a) identifying links between approximate models and their rigorous mathematical counterpart, (b) proving the exactness of certain epidemic models on tree-like networks, (c) highlighting linkages between various modern epidemic models, and (d) extending modelling to more realistic networks exhibiting clustering and motifs. He has also worked on applications of network science to areas such as the spread of innovations, livestock diseases, neuronal networks, network inference, spread of COVID19 involving vulnerable people such as single household parents and those receiving domiciliary care, with more recent work focusing on power and shipping networks.

Following a PhD in Applied Mathematics from University of Leeds, Prof Kiss has completed a four year position as a postdoctoral research fellow at the University of Oxford where he used network science to develop data-driven models of the spread of foot and mouth disease and other livestock diseases, such as scrapie and bovine TB.

Prof Kiss has also fulfilled a few important administrative roles at University of Sussex; namely he was the Director of Research and Knowledge in the School of Mathematical and Physical Sciences (Aug 2019 – July 2022) and was heavily involved in an extremely successful REF2021 (Research Excellence Framework 2021, UK) for both Mathematics and Physics Departments. Previously, he was the Impact and Knowledge Exchange Lead in Mathematics and was responsible for identifying and managing the completion of a suite of impact case studies which are an integral part of the REF submission. More recently, he acted as the Director of Research in Mathematics (September 2022 - May 2023). He is also a Fellow of HEA (Higher Education Academy, 2010 - present) and in recognition of the quality of his teaching he has won Teaching Awards at Sussex in 2010, 2014 and 2022.

He has published over 100 papers in peer-reviewed conferences and journals, four book chapters, and a monograph titled “Mathematics of network epidemics: from exact to approximate models” in the Interdisciplinary Applied Mathematics series of Springer (2017).