I am Lecturer at the School of Computing and Information Systems, University of Melbourne, Australia. My research interests are data mining, machine learning, temporal modelling, stochastic processes and user behaviour pattern analysis.
Before joining the University of Melbourne, I was Associate Lecturer at UTS, and a postdoctoral research fellow in data analytics at Data61 (formerly NICTA), CSIRO. I completed my PhD in the area of data mining and machine learning at the University of Sydney, 2017. My PhD supervisors are Associate Professor Irena Koprinska from the School of Computer Science, at the University of Sydney and Associate Professor Bin Li from Fudan University. I was awarded the Springer Theses Award in 2019 and Google PhD Fellowship in Machine Learning in 2017.
During my PhD studies, I designed and applied novel techniques for customer behaviour modelling. Our models can discover temporal patterns of customer behaviour, segment customers based on the purchase behaviour, track the evolution of customer groups e.g. merging and splitting over time, and evaluate the impact of marketing strategies.
I received my Bachelor of Engineering (Software Engineering) with Honours Class I in 2012 from the University of Sydney.
Recent work: "Dynamic customer segmentation viahierarchical fragmentation‑coagulation processes" has been published in Machine Learning journal [paper]
A survey on "Bayesian Nonparametric Space Partitions" has been published at IJCAI-21 Survey Track [paper]
Ling has published a book "Temporal Modelling of Customer Behaviour" in Springer Theses series. [book][e-book]
Ling was awarded the Google PhD Fellowship in Machine Learning in 2017. [link]
Our IJCAI-17 paper "Tracking the Evolution of Customer Purchase Behavior Segmentation via a Fragmentation-Coagulation Process" has been highlighted in the press release opening the conference. [news]