Abstract

Mobility patterns and interactions sensed by mobile phones provide a new source for many applications both in research and industry. In this talk, I will discuss two mobile sensed data-driven applications, one based on mobility patterns and the other based on interaction patterns.

The study of such human-centric massive datasets requires new mathematical models. A novel probabilistic topic model will be presented, the distant n-gram topic model (DNTM), which has been developed to address the problem of learning long duration human location sequences. The DNTM is based on Latent Dirichlet Allocation (LDA) and is advantageous for mining human behaviour patterns for many reasons.

Human interactions sensed ubiquitously by cellphones can benefit many domains, particularly for monitoring the spread of disease. A community of 72’s flu patterns have been collected simultaneous to their interactions sensed by mobile phone Bluetooth logs. The focus of this work is to determine the accuracy of incorporating interaction data into dynamic epidemiology models for infection prediction. We obtain errors of less than 2 infected people on average (when predicting the number of infected people over time considering a population of 72 people) and precisions of approximately 30% (when predicting exactly which individual was infected at a given time).

Biography Kate (Katayoun) Farrahi is a lecturer at the University of London, Goldsmiths. Her research focuses on large-scale human behavior modeling and mining, with special interest in data science, computational social sciences, mobile phone sensor data, and machine learning. Farrahi received her Ph.D. in Computer Science from the Swiss Federal Institute of Technology (EPFL) Lausanne, and the Idiap Research Institute, Switzerland. She has spent time as an intern at MIT and is a recipient of the Google Anita Borg scholarship, and the Idiap research award.