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LEARNING AT NORTHEASTERN UNIVERSITY LONDON

Engage with the world, from the vantage point of a culturally diverse global city.

Network Science Institute London

The Network Science Institute’s London hub was established in 2022 as the university’s European centre of the fast-growing research field. The institute’s world-leading researchers in London focus on scaling network science research globally, particularly in human health and global security. 

Research areas

Urban dynamics and computational social science 

Our research in urban dynamics and computational social science explores the intricate relationships between cities and their citizens through advanced data analysis and modelling. In an increasingly interconnected world, individuals generate vast amounts of digital data through their activities, interactions, and movements. We leverage the digital fingerprint of citizens to study human behaviour and social interactions. We adopt a multidisciplinary approach that combines elements of complex science, network theory, and machine learning to better comprehend the complex ecosystem shaped by people’s actions and interactions in cities and online. 

Network neuroscience and health

Our research focuses on exploring how artificial intelligence systems make sense of the world based on sparse observations, and how they learn structures and symmetries. Networks and related topological approaches provide a rigorous mathematical framework to study the symmetries and transformations inherent in information processing within complex systems. By leveraging these concepts, we aim to unravel the intricate relationship between the networked structure of functional spaces within neural networks with their performances, limits, and robustness. 

Fundamental network science

Despite outstanding theoretical advances over the past two decades, many problems still lie open both in theoretical areas, as well as in existing fields of application of network science. This is compounded by the recent emergence of generalised network structures, such as multilayered, temporal, and higher-order networks. In addition, empirical observations and intuitions often unravel surprising results whose theoretical/rigorous underpinning are not evident yet. There is therefore ample scope for principled and rigorous reasoning. Our research aims to close the gaps between theory and applications.

Utility networks

Much of the infrastructure around us, both real and virtual, such as telecoms, shipping and power networks are amenable to be modelled as networks or be analysed using tools originating in network science. By leveraging such tools, our research unravels patterns and understands interdependencies between network properties and system performance to aid better design and efficient interventions. 

Our team in London

Faculty

Researchers

PhD students

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