Day-to-day urban existence is increasingly mediated by digital technologies. The digital networks supporting this have increasingly become platforms of social feedback and interaction among users, leading to drastic changes in social networks. The penetration of such digital tools is particularly dense in urban environments creating digital-social networks that either remain confined within a platform or have a growing influence across platforms and beyond.

Digital networks are recognized as the key enabler for many emerging technologies, including those that expose individuals to one another’s behaviors, opinions, and feelings. At the same time, social networks are self-organizing systems that channel information and influence behavior. 

The goals of this research cluster are threefold. First, to investigate questions regarding the formation, maintenance, and self-organization of digital-social networks. Second, to develop and pilot theoretically informed interventions targeting outcomes of sustainable living both from an environmental and a social perspective. Third, to study the expansion of such piloted interventions to larger-scale field implementations of new technologies.


Privacy-preserving Intra-City Collaborative Learning for Health Screening Applications

Led by Prof. Farah Shamout & Prof. Michail Maniatakos

This project aims to develop a privacy-preserving collaborative learning framework that can be applied in UAE nationwide programs that rely on data exchange, such as health screening. The work involves the development of (i) a collaborative learning framework that enables optimal training of deep neural networks, the backbone of the AI decision-support tools, across multiple parties, and (ii) a data encryption technique to facilitate secure data exchange. The team will focus on a real-world setting using data collected from multiple hospital facilities in Abu Dhabi. This study will create the basis for a novel secure data transfer and collaborative learning framework that can facilitate the exchange of a wide variety of sensitive data within smart cities.