My research lies at the intersection of Bayesian statistics and machine learning, with interests in Bayesian nonparametrics, statistical network modelling, stochastic and point processes, and scalable Bayesian inference.
I develop Bayesian nonparametric models for sparse and heterogeneous networks, focusing on completely random measures, power-law degrees, overlapping communities, and networks that evolve over time. Current work includes dynamic, bipartite and covariate-dependent networks.
I develop flexible models for event data across space and time, particularly Hawkes and Cox processes, combining stochastic-process modelling with Gaussian-process and Bayesian nonparametric methods.
I work at the interface of machine learning and statistical methodology, including generative models, representation learning and interpretability. A newer direction explores neural and amortized methods for accelerating Bayesian inference while retaining principled uncertainty quantification.
Open-source software and computational tools accompanying my research.
Earlier work applying statistical and machine learning methods to epidemic surveillance and public health, developed at Imperial College London during the COVID-19 pandemic, spanning genomic epidemiology, real-time nowcasting and branching-process models of disease spread.