Lorenzo is a scientist working at the intersection of artificial intelligence, Earth observation, and natural hazard science. His work focuses on understanding how complex hazard systems evolve, and on improving the way we monitor, forecast, and manage cascading mountain hazards and risks.

He also serves as Chair of the Working Group for Educational Materials within the UN Global Initiative on Resilience to Natural Hazards through AI Solutions, coordinating international educational activities on AI applications for disaster risk management.

Prior to joining King's, Lorenzo worked as a Research Associate at the University of Cambridge, with a joint appointment in the Departments of Earth Sciences and Geography. He earned a PhD in Geosciences from the University of Padova and an MSc in Geotechnologies for Environmental Applications from the University of Florence.

Research

• AI-driven prediction of gravity-driven runout processes (e.g., landslides, avalanches, GLOFs...)
• Detection and monitoring of slow-moving landslides
• Forecasting landslide displacement and failure dynamics
• Landslide disaster response and risk-informed decision support

Lorenzo’s research focuses on gravity-driven hazards, particularly landslides and related mass movement processes. He studies how these systems evolve, interact, and sometimes cascade into larger disasters. His work includes predicting the runout of mass flows such as landslides, avalanches, and flash floods, developing automated all-weather methods to detect slow-moving landslides, and forecasting landslide motion for early warning and disaster response. Much of this research combines Earth observation data, artificial intelligence, and physical modelling to improve monitoring and hazard assessment.

PhD supervision

Lorenzo welcomes applications in the following areas:

• AI-driven detection and monitoring of landslides and slope instability
• Forecasting landslide displacement and failure dynamics
• Modelling gravity-driven mass flows and runout processes
• Multi-hazard and cascading risk modelling in mountain environments
• Probabilistic prediction and uncertainty quantification in hazard forecasting
• Earth observation for disaster response and early warning systems