Active Learning for event detection in large-scale information networks
MoRE2020 Fellow Qing Zhao, incoming mobility from Cornell University, USA, to Chalmers University of Technology
The problem of detecting rare events of interest in massive data streams and large complex networks is ubiquitous. The rare events may represent opportunities with exceptional returns or anomalies associated with high costs or potential catastrophic consequences. This project addresses the problem of detecting rare events as quickly and as reliably as possible when the total number of hypotheses is large, the observations are noisy, and the prior knowledge on the rare events may be as little as "they are different from the nominal." We aim to establish fundamental theory, performance limits, and adaptive algorithms with scalable computation complexity and guaranteed performance. The scientific methodologies of this research lie in the intersection of active inference, machine learning, graph theory, and information theory. This project is a systematic study of a class of problems most relevant in the era of increasing network size and abundance of data. Specific applications include anomaly prevision in radio access networks of next-generation wireless communication systems, Internet traffic monitoring and engineering, anomaly detection in vehicle fleet data and multi-contingency analysis in energy systems.
Collaborating end-users: Ericsson AB, Volvo Car Corporation