Publications
Network/Graph Modelling
Dynamic network modelling using completely random measures, in progress, 2023
Exchangeable Random Measures for Sparse and Modular Graphs with Overlapping Communities, Journal of the Royal Statistical Society B, 2020
A unified construction for series representations and finite approximations of competely random measures. Bernoulli, 2020
Arrival time augmentation for series representations and finite approximations of completely random measures, Tractable Probabilistic Modelling, International Conference in Machine Learning, 2019.
Modelling sparsity, heterogeneity, reciprocity and community structure in temporal interaction data., NeurIPS 2018
Matlab Software Package for simulation and inference on sparse graphs with overlapping communities, 2017.
Stochastic Processes
Unifying the effective reproduction number, incidence, and prevalence under a stochastic age-dependent branching process, Mathematical Biology, 2023.
Spatiotemporal Cox-Hawkes, Transactions of Machine Learning Research, 2022
Gaussian Process Nowcasting: Application to COVID-19 Mortality Reporting, AISTATS, 2021.
Deep Generative Models
Multi-temporal dynamic representations for longitudinal social media posts, in progress, 2023
On memorisation and influence function in neural networks, in progress, 2023
Deep Survival Analysis: Nonparametrics and MIssingness. Proceedings of Machine Learning Research, 2020
Relaxed-Responsibility Hierarchical Discrete VAEs, Workshop on Bayesian Deep Learning, ICML, 2021
COVID-19 modelling
Genomics and epidemiology of a novel SARS-CoV-2 lineage in Manaus, Brazil, Science 2021.
Changing composition of SARS-CoV-2 lineages and rise of Delta variant in England, The Lancet, 2021.
Factors driving extensive spatial and temporal fluctuations in COVID-19 fatality rates in Brazilian hospitals, Nature Medicine, 2022