Today's mobile phones are far from mere communication devices they were ten years ago. Equipped with sophisticated sensors and advanced computing hardware, phones can be used to infer users' location, activity, social setting and more. As devices become increasingly intelligent, their capabilities evolve beyond inferring context to predicting it, and then reasoning and acting upon the predicted context. Information about users’ behaviour can also be gathered by means of wearables and IoT devices as well as by sensors embedded in the fabric of our cities. Indeed, other sources include interactions with online services, social media and user-generated content. In general, data can be collected not only about individuals but also about groups, communities, cities and even entire nations: this data can then be used to build prediction models and what-if scenarios at different levels of resolution in space and time. The potential applications are several, from urban planning to epidemiology, from marketing to positive behaviour intervention. These efforts can be seen as part of the emergent area of anticipatory mobile computing, a cross-disciplinary field that harnesses mobile technologies and machine learning for intelligent reasoning based on the prediction of future events.

In this talk I will give an overview of our recent research efforts in this area and I will also discuss examples of practical applications, in particular in the area of digital health, we are working on. I will also examine the critical aspects related to privacy of personal information used as a basis for these systems, including the inherent risks and potential solutions.

Mirco Musolesi is a Reader (equivalent to an Associate Professor in the North-American system) in Data Science at University College London and a Turing Fellow at the Alan Turing Institute, the UK national institute for data science. At UCL he leads the Intelligent Social Systems Lab.  He held research and teaching positions at Dartmouth, Cambridge, St Andrews and Birmingham. He is a computer scientist with a strong interest in sensing, modelling, understanding and predicting human behaviour and social dynamics in space and time, at different scales, using the "digital traces" we generate daily in our online and offline lives. He is interested in developing mathematical and computational models as well as implementing real-world systems based on them. This work has applications in a variety of domains, such as intelligent systems design, ubiquitous computing, digital health, security&privacy, and data science for social good.

More details about his research profile can be found at: http://www.ucl.ac.uk/~ucfamus/