While Deep neural networks (DNNs) succeed in exploiting non-linear patterns in very large and high-dimensional datasets, they catastrophically fail without warning under dataset shift, i.e., changes in data distribution. This PhD project will study various ways to resolve this pitfall by characterising, detecting, and generalising against dataset shift. The research will theoretically unify sparse and inconsistent literature, and empirically validate that theory in the application of genomics, with results to inform ways to maximise reliability of learning systems under datasets shifts.
The project focuses on developing techniques and methodologies grounded in unsolved challenges in computational biology and multi-modal healthcare data. The project commenced in October 2021 with the recruitment of the Centre’s first PhD Researcher, Sam MacDonald, who is based at The University of Queensland. Sam is investigating the proposed methodologies on real datasets from different healthcare organisations, and is supervised by Chief Investigator Dr Fred Roosta-Khorasani from the School of Mathematics and Physics (UQ) and Dr Quan Nguyen from the Institute for Molecular Bioscience (IMB) at UQ.
The team collaborated with Max Kelsen, a Brisbane based artificial intelligence and software engineering agency, during 2021 to early 2023. Max Kelsen was acquired by Bain & Company in 2023.