Rate My Professor Alberto Ardid

Alberto Ardid is a Lecturer (Associate Professor) in Civil and Environmental Engineering at the University of Canterbury. He is a geophysicist and data scientist specialising in Environmental Informatics. His research develops probabilistic and machine learning models for natural hazard forecasting of volcanic eruptions, wildfires, snow avalanches, and floods by integrating time series analysis, Bayesian inference, and deep learning with real-time observational data. These tools are designed to generalise across hazard systems and to be deployable in under-resourced monitoring settings. Ardid’s work on volcanic eruption forecasting includes ergodic seismic precursors and transfer learning approaches that identify transferable eruption precursors across diverse volcanic settings, even at data-scarce volcanoes. He has also advanced sub-hourly wildfire potential forecasting using machine learning on time series of surface weather variables and contributed to quantifying heat transfer in geothermal systems through multi-channel data modelling.

Ardid earned a BSc in Geophysics (2013) and an MSc in Geophysics (2016) from the University of Chile, followed by a PhD in Engineering Science from the University of Auckland in 2020. He joined the University of Canterbury as a PhD student, served as a Postdoctoral Researcher in Civil and Environmental Engineering from 2022 to 2025, and was appointed Lecturer in 2026. His major awards include the New Zealand Geophysics Prize in 2025 for outstanding research publication in geophysics related to New Zealand, the Allianz Climate Risk Award in 2024 for an innovative machine-learning-based wildfire danger forecasting system, and appointment in 2026 as Chair of the Topic Group on AI for Volcanic Eruption Forecasting within the United Nations Global Initiative on Resilience to Natural Hazards through AI Solutions. He has delivered invited keynotes on AI-powered wildfire forecasting. Key publications include “Ergodic seismic precursors and transfer learning for short-term eruption forecasting at data-scarce volcanoes” in Nature Communications (2025) and “Seismic precursors to the Whakaari 2019 phreatic eruption are transferable to other eruptions and volcanoes” in Nature Communications (2022). Ardid supervises postgraduate students in eruption forecasting, wildfire prediction, avalanche detection, flood modelling, and geothermal prospectivity, and his teaching emphasises Python programming, numerical modelling, inversion techniques, and machine learning applications in geoscientific and engineering contexts.