Research Interests

My research interests lie in the intersection between applied and methodological statistics. My past projects focused on missing data, predictive analytics, extreme value analysis and point process modelling.

I’m particularly interested in developing efficient inference methods for non-standard data generating mechanisms to better model the rich, complex data sets that arise from environmental and industrial processes.

Past Projects

Inference for extreme earthquake magnitudes accounting for a time-varying measurement process. (arXiv) Motivated by earthquake catalogues, we consider variable data quality in the form of rounded and incompletely observed data. We develop an approach to select a time-varying modelling threshold that makes best use of the available data in an extreme value analysis, accounting for uncertainty in the magnitude model and for the rounding of observations.

Statistical Modelling of Induced Earthquakes (PhD Thesis). My PhD thesis focused on how to model anthropogenic earthquakes while making best use of the limited available data. Firstly, a selection of physically-motivated parametric models are explored for describing the link between gas extraction and induced earthquake locations. Secondly, new inference methods are developed that allow improvements to the earthquake detection network to be included when modelling extreme earthquake magnitudes. Finally, a reparameterised and extended version of the Epidemic Type Aftershock model introduced. This both allows for more efficient inference and relaxes the common assumption of independent and identically distributed magnitudes.

A review of simulated annealing techniques: Simulated annealing is a metahuristic technique mainly used for combinatorial optimisation. Applications, parallelisation and extensions of the technique were reviewed.

Inference on censored networks: Networks are censored when existing nodes or edges are not observed. Methods for inference under different types of missingness were explored. Master’s project supervised by Dr. Christopher Nemeth.

Computionally intensive methods for modelling houshold epidemics: Approximate Bayesian Computation was utilised to allow inference on disease models with intractable likelihoods. Master’s dissertation supervised by Prof. Peter Neal.

Conference and workshop contributions

Date Event Location
Nov 2023 RSS Local Group Meeting Bath, UK.
Sep 2023 Royal Statistical Society conference Harrogate, UK.
Sep 2023 RSS pre-conference workshop Harrogate, UK.
Jun 2023 IMA Idea Exchange: Mathematicians and Statisticians Teaching in Higher Education Remote.
Sep 2022 Royal Statistical Society conference Aberdeen, UK.
Jun 2022 M_max workshop Amsterdam, NL.
Jan 2021 CRG Extremes workshop Remote.
May 2020 STOR-i time-series and spatial statistics workshop Remote.
Sept 2019 Interfaces in extreme value theory workshop Lancaster, UK.
Sept 2019 Royal Statistical Society conference Belfast, UK.
Aug 2019 International statistical seismology workshop (StatSei11) Hakone, JPN.
Jul 2019 GRASPA (Italian Environmetics Society) Pescara, IT.
Jan 2019 STOR-i annual conference Lancaster, UK.
Jan 2018 STOR-i annual conference Lancaster, UK.

Research Project Supervision

I have had the good fortune to supervise some exceptional early career researchers in their postgraduate and undergraduate research projects. Below is a list of the students I have supervised along with their project titles.


  • Conor Murphy (Oct 2021 - Present) - Assessment of hazard and risk due to induced seismicity for underground CO2 Storage and oil and gas production assets.
  • Wanchen Yue (Oct 2023 - Present) - Statistical earthquake models to account for measurement errors and dependence.

MSc and MSci

Academic Year 2023-24

  • Brian Mac Carvill - Data privacy and security through homomorphic encryption.
  • Yinglai Qi - Investigating the risk of developing diabetes using UK Biobank data.
  • Etienne Caprioli - Analysing Nonstationary Hazard Dynamics (title tbc)
  • Ioannis Spanos - Analysing Nonstationary Hazard Dynamics (title tbc)
  • Xinran Huang - Analysing Nonstationary Hazard Dynamics (title tbc)
  • Xiyue Zhang - Analysing Nonstationary Hazard Dynamics (title tbc)
  • Zihan Yan - Analysing Nonstationary Hazard Dynamics (title tbc)
  • Jiahui Chen - Statistical modelling of earthquakes in the North Anatolian Fault Zone.
  • Jianjing Yu - Rare events in financial time series.

Academic Year 2022-23

  • Wanchen Yue - Statistical earthquake models to account for measurement errors and dependence.
  • Ash Bellett - Contextual bandits with non-stationary Gaussian process rewards.
  • Christian Liman - Multiple imputation for tree-based models.
  • Li Vern Teo - Catch me if you CNN: adversarial machine learning for detecting synthetic content.
  • Minjian Wu - Outlier detection and adaptation under covariate shift.
  • Paula Cordero Encinar - Representing ignorance about extreme earthquake magnitudes.
  • Lucy He - Measurement errors in earthquake modelling.
  • Shantianfang Gao - Inhomogeneous Poisson point process estimation using spline methods.

Academic Year 2021-22

  • Diana Xu - An extreme value mixture model for natural gas prices.
  • Hugo Barnett - Hypothesis testing with the self-inhibiting Hawkes process.
  • Nan Zhou - Self-driving vehicles road safety analysis by application of extreme value theory.
  • Xuan Hou - An extreme value analysis of rainfall in London.
  • Conor Murphy - Assessment of hazard and risk due to induced seismicity for underground CO2 Storage and oil and gas production assets.


  • Natalie Young - Point pattern analysis in statistical ecology.
  • Peter Greenstreet - Self-exciting point process models.