Emily Somerset

Emily Somerset’s research focuses on developing Bayesian hierarchical models for public health surveillance, with applications in wastewater-based epidemiology, epidemic curve reconstruction, forecasting hospital admissions and stays, and infectious disease dynamics (e.g. wastewater-based estimation of reproduction rates). Her work also applies differentiable Gaussian processes and Bayesian cut models in these and related contexts. Emily is also engaged in statistics education research, with a particular interest in the impact of generative AI on student learning, as well as the effects of flexible assessment structures on student writing and engagement.