Decoding User Behaviour from Smartphone Interaction Event Streams This is my PhD thesis, it compounds my individual research over the years into one document.

Discovering Types of Smartphone Usage Sessions from User-App Interactions In this paper, we examine how embedding physical user-app activity (e.g., taps and scrolls) can provide a rich basis for summarising device usage. Using a large dataset of 82,758,449 interaction events from 86 users over an 8-week period we combine feature embedding and unsupervised learning to extract prominent interactions within clusters of smartphone usage sessions.

Utilising the co-occurrence of user interface interactions as a risk indicator for smartphone addiction The study highlights a novel methodology to transform and analyse large amounts of interaction events to infer a user's level of smartphone addiction. This is a step forward from using commonly used metrics such as pure screen on time which can misrepresent the cognitive complexities and dependencies of human behaviour.