Sam Mitchinson

Sam Mitchinson


Sam completed his MSc in Volcanology and Geological Hazards in 2014. For the last seven years, Sam has been working within the global insurance market, in the field of Natural Catastrophe Modelling and Model Development. Sam joined Howden Group Holdings in 2020 where he has lead research and development projects on hail risk in North America and Australia as well as building a worldwide catastrophe modelling client base for the Group.

In 2019, Sam earned the CCRA designation, which is recognised in the insurance industry as a symbol of excellence in the field of catastrophe modelling. This year, he received the Chartered Geographer (CGeog) accreditation awarded by the Royal Geographical Society.

Sam aspires to build commercial level catastrophe models for regions where vulnerable communities are at risk to natural hazards, so that more affordable products can be made available to mitigate loss of property, which gives those impacted the capacity to recover quickly from catastrophic events.

Sam Mitchinson

PhD title: Machine Learning for Geophysical Hazard Sequences

Geophysical hazards, such as volcanic eruptions, large earthquakes and landslides, threaten millions of people around the world. Many of these hazards are unpredictable, but there are often precursors that may be used to forecast damaging events. These precursors could be in the form of monitoring observations such as seismicity, deformation and gas emissions, or from data processed after the event has occurred, recognised in hindsight.


In typical hazard forecasting, observations of pre-cursors are studied and researchers use their prior knowledge to estimate the probability of an event occurring. However, Machine Learning (ML) tools are increasingly being applied to geosciences and hazard monitoring due to ML’s strength in identifying patterns. If patterns can be identified in hindsight, then they could be used as a precursor to forecast future events.