I am a third-year DPhil student on the StatML CDT. I grew up in Texas and completed my undergraduate degree in Mathematics and Economics at The University of Texas at Austin. Before starting at Oxford, I worked as a forensic data analyst investigating financial crimes for the U.S. government. My hobbies include football (both British and American), science fiction, baking, and musicals.

My research focuses on causal inference and (Bayesian) predictive modelling. I am particularly interested in methods that can be applied to clinical trial design and analysis.

I am a second year DPhil student studying on the Modern Statistics and Statistical Machine Learning CDT. Following the completion of my undergraduate studies in my home-city of Christchurch, New Zealand, my research career was born within the COVID-19 pandemic. Between March 2020 and September 2021 I worked in a government-funded modelling programme, applying mathematical and statistical methods to directly inform New Zealand’s COVID-19 policy. It was this experience – using statistical methods in real time to inform policy – that lead me to pursue a DPhil. I have an undergraduate degree in Statistics and Financial Engineering from the University of Canterbury (2018), and a first-class honour’s degree in Applied Mathematics (2019) from the same university. In addition to being enrolled at University College, Oxford, I am also an affiliate student at Imperial College London.

Broadly speaking, my interests are in the statistical and mathematical modelling of infectious diseases. More specifically, I am interested in:

• Methodological improvements that allow us to make improved inferences about the state of infectious disease outbreaks
• The modelling-policy interface
• Real-time outbreak analysis, particularly of novel or recently imported infectious disease, and low-incidence scenarios

I am a third year DPhil student on the StatML CDT. The goal of my doctoral project is to model the humanitarian impact of disaster events such as earthquakes. I completed my undergraduate degree in Mathematics and Statistics at the University of Western Australia, and was awarded a Rhodes Scholarship in 2021. Outside of my studies, I enjoy swimming, ABBA/Taylor Swift club nights, and walks around University Parks with a good podcast.

My primary statistical interests are Bayesian statistics and MCMC methods. I am most driven by the applications of my work, and in the long run, would like to use statistics and/or machine learning to address societal or environmental challenges.

I rejoined the Department of Statistics as Director of Studies in 2008, having previously been a University Lecturer in the Department from 1992 to 2006. I was an undergraduate at the University of Cambridge and a graduate student in the Statistical Laboratory there. My PhD thesis was on dynamic routing in queueing networks, and subsequently I worked on related problems in both queueing and loss networks.

As Director of Studies, I undertake a range of duties related to academic management, strategy, examining and admissions, and contribute to teaching.

My work lies at the intersection of learning theory and optimization, with a particular emphasis on the mirror descent algorithm and its variants. I have previously studied Mathematics and Computer Science at the University of Oxford.

Statistical Learning Theory, Optimization

Background in Mathematics particularly Probability Theory and Analysis in infinite dimensions.

Deep Learning, Kernel methods, Wasserstein gradient flow, Generalised Bayesian inference and most mathematical aspects related to deep learning and uncertainty quantification.

I am a third year DPhil student working under the supervision of Robin Evans and Dino Sejdinovic.

My research focuses on applying causal inference to improve machine learning methodology. I have mostly focused on using these methods to improve the fairness and explainability of machine learning methods.