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Supervisor: Professor Emily So


Research overview: 

As the world marked the midterm of the Sendai Framework for Disaster Risk Reduction 2015-2030, the expensive large-scale operation of probabilistic catastrophe models to standardise exposure datasets (e.g., human settlements) across countries with different and lacking vulnerability characteristics (e.g., building material construction type) has remained the primary bottleneck to providing a reliable audit of the evolving risk landscape.

Early efforts in developing large-scale exposure datasets were able to map the distribution of human settlements and their vulnerabilities, which has been the basis of several global assessment reports. However, these datasets contain inherent biases that favour developed countries (e.g., least-developed countries have different and non-standard vulnerability characteristics because of the ubiquity of more informal settlements and different construction methodologies) and are increasingly outdated because of rapid urbanisation.

Our research investigates artificial intelligence methods to quantify exposure and risk of the built environment at large scales, and would enable the beginnings of a global risk audit, measuring the changes in disaster risk profiles over time to assess whether countries are making progress in reducing disaster risk. For updates, kindly visit our project website at



Joshua is a Filipino registered civil engineer and a postgraduate researcher at the AI4ER CDT to explore how artificial intelligence can improve our understanding of the global disaster resilience. He gained diverse expertise in civil engineering (BS, MS), public policy (MA), business (Executive Education), and environmental data science (MRes). He is a Stanford Knight-Hennessy scholar and has previously worked at Arup, Stanford Urban Resilience Initiative, Earthquakes & Megacities Initiative, and FM Global Engineering & Research. For updates, kindly visit