To assess the impact of these policies in reducing energy consumption in contrast with the energy needed to produce and install the equipment required to implement these dynamic policies, we will consider the policies we develop in the context of a Life Cycle Assessment framework.<\/p>\n\n\n\n
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MRes<\/h2>\n\n\n\n
The MRes year of the STOR-i programme involves research on a wide range of active research areas in statistics and operations research. In the first term of the course, we took part in four ‘sprints’ in which we were given a topic to research on the Monday morning, and in groups we presented our research on the Thursday afternoon. Topics for these sprints included MCMC, changepoint detection, dynamic programming, and the Travelling Salesperson Problem. We then wrote individual reports on a sprint of our choosing; my report on a variation of the Travelling Salesperson Problem can be found below. The second and third terms of the course involved writing both a technical and non technical report on two subjects from a list of Research Topics, which include statistical ecology, Bayesian & computational statistics, transport and logistics, and stochastic modelling and optimisation. Below you can find my research topic reports on multi-armed bandits and extreme value theory. In the fourth term, we will chose our PhD topics and write a research proposal, which forms the dissertation for the MRes.<\/p>\n\n\n\n
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![\"\"](\"http:\/\/www.lancaster.ac.uk\/stor-i-student-sites\/sophie-brimble\/wp-content\/uploads\/sites\/68\/2025\/05\/rt2-poster-pic-e1747159290550-1024x843.jpg\")
<\/figure>Extreme Value Threshold Selection with Mixture Models<\/strong> In extreme value modelling, we wish to model events which occur very rarely, like high river levels in flood modelling. This requires a new modelling frame- The Generalised Pareto Distribution can be used to approximate the distribution of such tail data, however in order to use likelihood inference to estimate the threshold above which this approximation is suitable, we must model the whole of the data. This motivates extreme value mixture models.<\/p>\n\n\n\n <\/p>\n\n\n\n Wrestling the Multi-Armed Bandit: Optimising Patient Allocation in Clinical Trials<\/strong> The Multi-Armed Bandit Problem is an established problem in Operations Research, which can be used in a Clinical Trials setting to allocate patients to treatment arms. <\/p>\n\n\n\n This report looks at variations of the Dynamic Programming solution to this problem as Clinical Trials allocation methods, comparing them to the traditional Randomised Control Trial allocation.<\/p>\n\n\n\n <\/p>\n\n\n\n A Review of the Fixed Destination Multiple Depot Multiple Travelling Salesperson Problem<\/strong> This is a literature review for the Fixed Destination Multiple Depot Multiple Travelling Salesperson Problem, a variation of TSP where we have multiple salespeople based at multiple depots, all of whom must visit at least one node before returning to their original depot.<\/p>\n\n\n\n <\/p>\n\n\n\n Below are some links to research I did whilst I was an MMath student at Durham University. This includes the research project I did on as a STOR-i Summer Research Intern, and my undergraduate dissertation on natural language processing.<\/p>\n\n\n\n <\/p>\n\n\n\n Topic Modelling for Text Data<\/strong> Topic modelling is an area of natural language processing in which we assume that in text data, there is an underlying latent topic variable. Each topic is a set of semantically related words, and text data is generated by sampling words from these different topics.<\/p>\n\n\n\n This report describes and evaluates several different topic modelling methods, and finally applies them to the Jane Austen novels to determine whether there is a difference in the underlying topics in the novels.<\/p>\n<\/div><\/div>\n\n\n\n <\/p>\n\n\n\n
STOR-i MRes Technical Report and Poster, supervised by Emma Eastoe, Lancaster University, 2025<\/em>.<\/p>\n\n\n\n
work, with a focus on the tail of the data distribution.<\/p>\n\n\n\n
STOR-i MRes Non-Technical Report and Presentation, supervised by Peter Jacko, Lancaster University, 2025.<\/em><\/p>\n\n\n\n![\"\"](\"https:\/\/www.lancaster.ac.uk\/stor-i-student-sites\/sophie-brimble\/wp-content\/uploads\/sites\/68\/2025\/03\/484589492_990451266382088_7467608381574969135_n.jpg\")
<\/figure><\/div>\n\n\n\n![\"\"](\"https:\/\/www.lancaster.ac.uk\/stor-i-student-sites\/sophie-brimble\/wp-content\/uploads\/sites\/68\/2025\/03\/mres-pic.jpg\")
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STOR-i Mres Sprint Report, supervised by Burak Boyaci, Lancaster University, 2025<\/em>.<\/p>\n\n\n\nUndergraduate<\/h2>\n\n\n\n
Master’s Dissertation supervised by Jonathan Cumming, Durham University, 2024.<\/em><\/p>\n\n\n\n
![\"Sophie](\"https:\/\/www.lancaster.ac.uk\/stor-i-student-sites\/sophie-brimble\/wp-content\/uploads\/sites\/68\/2025\/01\/472919567_851424603675272_832954585282611167_n.jpg\")
<\/figure><\/div>\n\n\n\n![\"Sophie](\"https:\/\/www.lancaster.ac.uk\/stor-i-student-sites\/sophie-brimble\/wp-content\/uploads\/sites\/68\/2025\/01\/internship-4.jpg\")
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