James Hannon

About

I recently completed a PhD in Statistics, with a research focus on applied Bayesian modelling for societal and behavioural data. My work sits at the intersection of statistical methodology and real-world applications, with an emphasis on building interpretable, decision-relevant models from complex and often noisy data sources.

My research spans predictive modelling using emerging data sources, including telematics and wearable sensor data, satellite and street-level imagery, and self-reported health and behavioural measures. Across these projects, I develop hierarchical and spatial models to capture heterogeneity, uncertainty, and dependence structures, and use simulation studies to explore the implications of modelling assumptions in applied settings.

More broadly, I am interested in how novel data sources can be responsibly integrated into risk modelling and decision-making, particularly in insurance, public policy, and related domains. A recurring theme of my work is translating advanced statistical methods into practical insights that are transparent, reproducible, and meaningful to non-technical stakeholders.