I am a first-year Doctoral (PhD) Candidate in the Department of Statistics at the University of Warwick. My research is jointly funded by the Central England NERC Training Alliance (CENTA), the Centre for Environment, Fisheries and Aquaculture Science (CEFAS)Link opens in a new window, and the university itself. Dr Richard EverittLink opens in a new window, from the Department of Statistics here at the University of Warwick, is my academic supervisor. Additionally, Dr Nicola WalkerLink opens in a new window from CEFAS serves as my industry supervisor. Prior to my doctoral studies, I obtained a BSc (Hons) in Mathematics and Statistics from the University of Warwick. I also hold an MSc in Advanced Computer Science from the University of Oxford. During my time at Oxford, I completed a thesis titled "Syntactic Bisimulation Methods on a Multifidelity Bayesian Verification Framework", under the supervision of Professor Alessandro AbateLink opens in a new window from the Department of Computer Science.
My academic pursuits predominantly lie at the intersection of Computational Statistics and Bayesian Inference, with a distinct emphasis on Monte Carlo methodologies. Within the ambit of my doctoral research, I am currently engaged in advancing the Pareto-Smoothed Importance Sampling (PSIS) method, specifically extending its capabilities to integrate within a Sequential Monte Carlo (SMC) framework. As an aside to my primary research focus, I harbour a keen interest in the domain of Algebraic Statistics. I am particularly enthused about delving deeper into the intricate relationship between algebraic geometry and its implications in statistical sciences.
Beyond my academic pursuits, I have a profound appreciation for Formula One and am an avid follower of the sport. Additionally, I enjoy sim racing, specifically on the F1 platform. I also take pleasure in playing badminton recreationally, finding it a great way to de-stress after a busy week!
September 2017 - June 2020: BSc (Hons) in Mathematics and Statistics @ University of Warwick
September 2022 - September 2023: MSc in Advanced Computer Science @ University of Oxford
October 2023 - present: PhD in Statistics @ University of Warwick
July 2023: Royal Institution Masterclasses in Computer Science Teaching Assistant @ Department of Computer Science, University of Oxford
April 2018: Technology Spring Week @ Barclays Bank PLC (United Kingdom)
July - September 2018: Group Corporate Development Intern @ Sunway Money Sdn Bhd (Malaysia)
June - August 2019: Technology Analyst Intern @ Barclays Bank PLC (United Kingdom)
June 2020 - September 2022: Graduate Technology Analyst @ Barclays Bank PLC (United Kingdom)
Relevant Extra-Curricular Activities
November 2017 - June 2019: Student-Staff Liaison Committee Student Representative @ Department of Statistics, University of Warwick
September 2022 - September 2023: Student Ambassador @ Department of Computer Science, University of Oxford
PhD Project - Inference of Ecological and Environmental Models
Fisheries modelling is in a constant state of progression. Presently, key areas of focus in the field encompass individual fish movement, the influence of environmental drivers, food-web dynamics, and interactions within mixed-fishery settings. These elements are stimulating the advancement of intricate and innovative fisheries models. Nevertheless, for these models to be deemed reliable for informing decision-making processes, it's imperative to have a clear comprehension of how they align with data, along with an awareness of associated uncertainties.
Approximate Bayesian algorithms present a noteworthy solution, offering a mechanism for statistical inference applicable to models with extensive complexity. More importantly, these algorithms furnish a structured method to gauge the degree of support that empirical data lends to specific models. The objective of my PhD research is to refine and expand upon these Bayesian techniques, tailoring them for use with computationally intensive multi-parameter fisheries models. By achieving this, the goal is to enhance the transparency and rigour of both parameterization and uncertainty quantification processes, thereby augmenting the applicability and relevance of these models in guiding decision-making.