Statistics Postgraduate Research Students
Learn about the statistics research undertaken by some of our current postgraduate research students.
Some of our current PGR students
Jack Kennedy
Jack’s research interests include elicitation of prior beliefs, statistical (Bayesian) analysis of computer code output, uncertainty analysis.
Takuo Matsubara
Takuo’s research interests are in Bayesian statistics, statistical machine learning, probabilistic numerics, reproducing Kernel Hilbert space. Their project covers Bayesian quadrature of neural networks.
Tao Ding
Tao’s current research interests are in functional data analysis, joint curve registration and classification/clustering with functional models.
Sophie Harbisher
Sophie’s research interests include Bayesian Optimal Design, Stochastic Gradient Descent, and Extreme Value Theory. Their proposed PhD thesis is titled Stochastic Gradient Descent for Bayesian Optimal Design in ODEs
Md Hossain
Md’s research interest is in Time Series Analysis. Their proposed PhD thesis is titled Dynamic Time Series Forecasting with Applications on Measuring Climatic & Hydrological Impact on Agricultural Productions in Bangladesh
Antonia Kontaratou
Antonia's research interests include Bayesian analysis, Hierarchical models, Scalable Bayesian computation, and Cloud computing technologies. Their research project is investigating Scalable Bayesian Hierarchical modelling with application in genomics
Ashleigh Mclean
Ashleigh's research interests include Bayesian Statistics, MCMC, and Stochastic Processes. Their proposed PhD thesis is titled Stochastic Differential Equation Driven State Space Models
Lauren Roberts
Lauren's research interests include Bayesian inference and Time series analysis. Their proposed PhD thesis is titled Real time monitoring and forecasting of time series
Rosabeth White
Rosabeth's research interests include Computational statistics, Bayesian inference, and Likelihood-free methods. Their proposed PhD Research Project is focussed on developing new summary statistics for Approximate Bayesian computation using composite likelihoods.