IIS Introductory Lecture: When AI Meets Networked World

Our world is networked where everyone and everything is connected. Networks are ubiquitous, including social networks, human interaction networks, gene regulatory networks, to name just a few. Machine learning and data analytics methodologies have been found useful in extracting hidden interaction and communication patterns from related network data. Applications include online user behavior characterization, user alignment across social networks, epidemic risk prediction, disease characterization, etc. In this talk, how related applications can be formulated as machine learning and data analytics problems will be presented and discussed.