I am a second year PhD student at the STOR-i Center for Doctoral Training (CDT) at Lancaster University.
Further information on my research, past projects and teaching can be found below.
Details of how studying in a CDT differs from a more traditional PhD can be found here.
Please feel free to contact me about any opportunities in applied statistics or data science (or the teaching thereof) using my contact form .
My research focuses on point process models, which describe the distribution in time and space of localised events. I consider how these models can be used to describe and predict earthquakes in the Netherlands.
|Research Interests: Methods||Research Interests: Applications|
My PhD is concerned with modelling induced seismicity by linking gas extraction to earthquake activity. Gas extraction has historically been variable with demand, both fluctuating within years and generally increasing over time. Compared to modelling tectonic earthquakes, this extraction pattern gives additional covariate information that can be drawn into the modelling procedure.
Models currently used for tectonic seismicity can struggle to represent induced catalogues, particularly models that include a non-constant triggering rate and after aftershock component. This is due to trade-off between parameters in the triggering and aftershock processes, as well as the inherently smaller catalogue sizes for induced events.
I consider how we can make most efficient use of the limited data that is available to us and how current models can be adapted so that parameters are more identifiable and predictive performance is improved.
MRes Statistics and Operations Research
Research Topic 2: Inference on censored networks.
Networks are censored when existing nodes or edges are not observed, either due to deliberate obscurification or because of the sampling scheme used for observation..
Research Topic 1: A review of simulated annealing techniques.
Simulated annealing is a metahuristic technique mainly used for combinatorial optimisation. Applications, parallelisation and extensions of the technique are reviewed.
MSci Mathematics with Statistics
Dissertation: Computer Intensive Methods for Modelling Household Epidemics.
Approximate Bayesian Computation is applied to provide inference for disease models with intractable likelihoods.
|2018/19||MATH562: Extreme Value Theory||Graduate teaching assistant|
|2018/19||MATH235: Statistics II||Graduate teaching assistant|
|2018/19||MATH240: Project Skills||Graduate teaching assistant|
|2018/19||MATH330: Likelihood Inference||Graduate teaching assistant|
|2018/19||MATH230: Probability II||Graduate teaching assistant|
|2017/18||STOR-i Internship: Supervision of project||Supervisor|
|2017/18||STOR-i Internship: Introduction to LaTeX||Co-leading short course|
|2017/18||DSCI485: Introduction to LaTeX||Co-leading short course|
|2017/18||MATH235: Statistics II||Graduate teaching assistant|
|2017/18||MATH465: Bayesian Inference||Graduate teaching assistant|
|2017/18||MATH330: Likelihood Inference||Graduate teaching assistant|
|2017/18||MATH230: Probability II||Graduate teaching assistant|
|2015/16||LAB100: Introduction to R||Graduate teaching assistant|
During my undergraduate degree I took the chance to study abroad at The University of Western Ontario in London, Canada. Ever since then I have thoroughly enjoyed tavelling to see more of the world, visiting and being visited by the friends I made on this trip.
When I returned to Lancaster I ran a project to help and promote other students to study abroad during their degrees. You can find out more about that project here.
As well as travelling, I like to relax by exercising. Since starting my PhD have been trying out distance running. Last year I completed Hexham and Kielder half-marathons and I am going run six more this year. I am doing this to raise money for the Great North Air Ambulance Service, have a look at my JustGiving page to find out more about what excellent work they do.