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CNN seminar - Understanding and Exploiting Social and Mobility Information from Big Mobile Data

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Abstract: Mobile phones are increasingly equipped with sensors, such as accelerometers, GPS receivers, and cameras, which can be used to sense and interpret people behaviour in real-time. Novel user-centered sensing applications can be built by exploiting the availability of these technologies. Moreover, data extracted from the sensors can also be used to model and predict people behaviour and movement patterns, providing a very rich set of multi-dimensional and linked data, which can be extremely useful, for instance, for the development of highly personalised applications, health interventions, and real-time support for policy-makers.

In this talk I will discuss some of our recent projects in the area of large-scale scale data mining and modelling of mobile datasets with applications to human mobility prediction and epidemic spreading containment. Indeed, the study of the interdependence of human movement and social ties of individuals is one of the most interesting research areas in computational social science. I will show how mobile phone data can be used to improve mobility prediction, by characterising and exploiting the correlation between movements of friends and acquaintances. This can be seen as a process of discovering correlation patterns in networks of linked social and geographic data. I will also discuss how data from mobile operators can be effectively exploited to model epidemic spreading and devise effective containment strategies.

Bio: Dr. Mirco Musolesi is a Senior Lecturer at the School of Computer Science at the University of Birmingham. He received a PhD in Computer Science from University College London in 2007. Before joining Birmingham, he held research positions at Dartmouth College and Cambridge and a Lectureship at the University of St Andrews. His research interests lie at the interface of different areas, namely ubiquitous computing, large-scale data mining, and network science. More information about his research profile can be found at the following URL :

This talk is part of the Cambridge Networks Network (CNN) series.

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