Professor Junbin Gao began his research career by studying approximation theory and application of multivariate spline functions in numerical solutions for partial differential equations, continuing research work in wavelet applications in chemometrics, and becoming an outstanding researcher in machine learning, pattern recognition, Bayesian learning/inference, numerical optimisation and big data analytics in business.

One of interesting examples in Junbin’s recent research is to propose matrix neural networks and apply it in longitudinal relational data in politics research, where he further develops it to tensorial recurrent neural network. In a series of papers from 2014 to 2017 Junbin Gao showed how to conduct data subspace clustering and dimensionality reduction on manifolds particularly for the abstract Grassmann manifolds. Much of this work has been joint with a number of international collaborators. Junbin Gao’s work prior to 2014 is on dimensionality reduction, which was funded by the Australian Research Council (ARC), and the success can be seen in a series of paper between 2005 and 2013 and the research was quoted by The Australian newspaper in 2012.