NetSI Hubs

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).

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.

Naomi is a Postdoctoral Research Assistant at the Network Science Institute of Northeastern University in London. Her research focuses on making sense of large connected systems by representing them as temporal graphs and developing algorithms and tools for their analysis. Most recently, this has been applied to using temporal motifs to understand cryptocurrency transaction networks and communication patterns in online social networks. Previously, she was a researcher for the Raphtory project funded by the Alan Turing Institute’s Tools, Practices and Systems stream. She obtained her PhD in Computer Science at Queen Mary University of London within the Networks group, under the supervision of Richard Clegg and Raul Mondragon.

Her research has been presented at a number of national and international venues including NetSci, the Conference on Complex Systems and ACM CSCW, and in 2019 she was awarded best PhD talk at the Cosener’s Multi-Service Networking workshop. In 2023 she co-organised the first workshop on Networks and Time based in London.

Alongside her main research she is a part of the open source community: she is a developer for the FETA (Framework for Evolving Topology Analysis) and Raphtory open source software for temporal network modelling and analysis, respectively.

Zsófi Zádor (she/her) is a Postdoctoral Researcher in the Network Science Institute at Northeastern University London. With a blend of expertise in social sciences and network science, Zsófi is interested in understanding inequality in human dynamics, particularly in cities and regions. Her recent focus is on examining gendered disparities in mobility.

Zsófi has a background in economic geography and economic networks. She completed her PhD at the University of Greenwich. Her doctoral research centred around trade networks, employing a combination of economic theory and a network perspective to understand the embeddedness and brokering role of regions in Global Value Chains. Previously, she earned an MA from the Central European University and was a member of the ANET Lab, Budapest. It was during this formative period that she became passionate about combining theories of economics and geography with approaches from network and computational science.