Bio
I am a doctoral student on the Modern Statistics and Statistical Machine Learning CDT at the University of Oxford, supervised by Professor Christl Donnelly and Dr Kris V Parag.
My thesis focusses on robust statistical methods for analysing infectious disease dynamics with an emphasis on flexible methodology and valid uncertainty quantification. My research combines Bayesian inference, sequential Monte Carlo methods, and epidemiological modelling, with applications to standard epidemiological data, as well as survey data and wastewater-based surveillance.
During my studies, I have also worked as a research assistant in the The Global Reference Group for Children Affected by Crisis, estimating orphanhood in Brazil during the COVID-19 pandemic (publication coming soon), and have held a variety of tutoring positions (departmental, college, and inter-departmental courses).
Past experience includes modelling for policymakers during the COVID-19 pandemic in my home country of New Zealand (March 2020 - September 2021), a quantitative trading internship at Optiver in Sydney (November 2019 - January 2020), and a data analysis internship at a startup in Bangkok (January - February 2018).
Education
University of Oxford | Oxford, United Kingdom
DPhil in Statistics | 2021 - 2025
University of Canterbury | Christchurch, New Zealand
BSc (Hons) Compuational and Applied Mathematics | 2019
Awarded with first class
University of Canterbury | Christchurch, New Zealand
BSc Financial Engineering and Statistics | 2016 - 2018
GPA: 8.96 out of 9
I also studied statistics at National University of Singapore on an exchange programme in 2018.