Courses at University of Canterbury and postgraduate supervision
My teaching bridges geophysics, data science, and environmental engineering. I aim to equip students with modern computational and modelling skills: Python programming, Bayesian methods, machine learning, and time series analysis, grounded in real physical systems and societal problems.
Research-informed teaching is central to my approach. Students in my courses work with real hazard datasets (seismic, weather, streamflow) and are introduced to state-of-the-art methodologies directly linked to my group's research publications.
~30 department academics, Civil & Natural Resources Engineering, with ongoing individual support
QuakeCoRE Emerging Researchers
Geology students, School of Earth and Environment, University of Canterbury
Multi-Data Integration to Improve Short-Term Eruption Forecasts Using Machine Learning
PhD · University of Canterbury
Primary Supervisor2026 – present
Physics-based modelling of hydrothermal eruption precursors
PhD · University of Canterbury
Co-Supervisor2026 – present
Geothermal prospectivity for gold and other relevant minerals using GIS and data-driven modelling
PhD · University of Canterbury
Co-Supervisor2026 – present
Advancing Avalanche Detection and Forecasting Using Geophysical Monitoring and Machine Learning
PhD · University of Canterbury
Co-Supervisor2026 – present
Data Science for Wine Fermentation
PhD · Lincoln University
Co-Supervisor2026 – present
Whakaari gas flux: pulsatory degassing in the lead up to the 2019 eruption
MSc · Civil & Environmental Engineering, UC
Co-Supervisor2026 – present
Volcanic Eruption Forecasting at Tungurahua Volcano
MSc · Geosciences
Co-Supervisor2026 – present
I regularly supervise Honours, Masters, and PhD students working at the intersection of geophysics, machine learning, and environmental hazard science. If you are interested in joining the group, please get in touch with a brief description of your background and research interests.
Current areas of interest for new students include:
Students from geophysics, engineering, data science, mathematics, or related fields are welcome. Strong programming skills (Python preferred) are an advantage.