2026 Outstanding PhD Theses Awardees
Citation for Dr Elizabeth Harris
Institution: The University of Newcastle
Thesis Title: Leverage Score Sampling for the Minimum Volume Covering Ellipsoid Problem
Elizabeth’s thesis developed new theoretical insights and practical algorithms that make the Minimum Volume Covering Ellipsoid problem scalable for large, high dimensional datasets. This problem has applications across: statistics; control theory; computational geometry; computer graphics; and engineering. Elizabeth’s work combined mathematical analysis, algorithm design, and extensive computation, showing how modern randomised methods can be applied to classical optimisation problems with a broad impact.
Citation for Dr David Morselli
Institution: Swinburne University of Technology and Politecnico di Torino
Thesis Title: Improving the effectiveness of oncolytic virotherapy: insights from mathematical modelling
David’s thesis used mathematical modelling to study Oncolytic viruses. Which are viral particles that specifically infect cancer cells, while mostly preserving healthy tissues. The main aim of David’s work was to develop mathematical models to study the spatial dynamics of infections by Oncolytic viruses, with a special emphasis on the role of stochastic events. This modelling work will help to evaluate phenomena which currently affect the use of Oncolytic viruses as a cancer treatment.
Citation for Dr Sandy Spiers
Institution: Curtin University
Thesis Title: Exact cutting plane methods for quadratic programming problems with applications
Sandy's thesis sought to bridge the methodological divide between, concave and non-concave optimisation problems. This was achieved by adapting cutting plane methods, which are usually reserved for concave problems, for use on nonconcave mixed-integer quadratic programming problems. The work also opened up new avenues for advancements in cutting plane methods in general. These new methods were applied to the important application of maintenance scheduling for refinery operations.


