Developing Behavioural Models for Intelligent User Interaction using Mobile and IoT Sensor Data
In this talk I will present our current work on extracting information about physical and emotional context through sensor data collected by means of mobile phones, wearables and other Internet of Things devices in order to deliver the right information at the right time to users. In particular, I will discuss how it is possible to extract behavioural signals from sensor data collected by means of off-the-shelf devices in order to drive the delivery of relevant information to users. I will then discuss the current challenges (and opportunities) in this emerging research area.
Mirco Musolesi is a Reader (equivalent to Associate Professor in the North-American academic system) in Data Science at University College London and a Turing Fellow at the Alan Turing Institute, the UK National Institute for Data Science and Artificial Intelligence. 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, and digital health & wellbeing.