Thesis Title
Modeling Heterogeneous Time-series with Multi-resolution Sporadic Data.

Research Description
This research investigates issues of multivariate time-series analysis for real-world data. The main contributions focus on providing new methods and algorithms to handle unwell-studied matters related to applying machine learning models to mobility, health, and urban system in the real world. In particular, the project addresses sequence data with heterogeneous and inconsistent characteristics collected from different sources and on varying temporal resolutions.

Supervisors
Prof Flora Salim, University of New South Wales
Dr Hao Xue, University of New South Wales
Dr Yongli Ren, RMIT University