Current Projects
Extreme Value Theory for Dynamical Systems with Physical Observables
This research examines ergodic dynamical systems representing simplified physical systems to establish statistical properties of maxima and minima, and to understand rare event returns. While classical extreme value results exist for standard observables, this project extends extreme value theory to physically meaningful quantities such as energy and vorticity that are relevant to climate science.
The observables studied have either Fréchet (fat-tailed) or Weibull (bounded) distributions, with the primary motivation being to give estimates of the probabilities of sustained periods of weather anomalies such as heatwaves, cold spells, or prolonged periods of rainfall in climate models.
Rare Event Analysis and Machine Learning Applications to SARS-CoV-2
This project applies extreme event analysis and machine learning to infectious disease research, conducted in collaboration with the Short Lab (Dr. Kirsty Short) at UQ. Key outcomes include the development of host transcriptomics and machine learning approaches for predicting secondary bacterial infections in patients with COVID-19.
The research also encompasses modelling SARS-CoV-2 evolution in cell cultures, investigating post-acute sequelae of COVID-19 including cardiovascular symptoms, and exploring anti-viral immune responses through serum biomarker analysis.
Statistical Modelling of Extreme Weather Events in a Changing Climate
This initiative addresses how extreme weather phenomena—tropical storms, wildfires, heatwaves, and large-scale floods—are affected by a changing climate. The research combines extreme value theory, numerical global circulation models, classification algorithms, and real-world observational data to develop regional extreme weather models.
The project assesses how climate cycles and elevated CO₂ levels affect extreme event probabilities, with specific applications including hurricane simulation, multiday precipitation modelling across eastern Australia, nonstationary temperature extreme analysis in Texas, and robust regional clustering of summer temperature extremes across Germany.