I am a postdoctoral researcher in the Epidemiology of Emerging and Zoonotic Infections funded by a National Institute for Health and Care Research grant as part of the Health Protection Research Unit (HPRU) in Emerging and Zoonotic Infections and supervised by Professor Christl Donnelly. 

I hold a PhD in infectious disease epidemiology and a Masters in Epidemiology, both from Imperial College London. I also hold a Bachelor in Veterinary Medicine from the Royal Veterinary College and worked in veterinary clinical practice for many years before returning to academia in 2016. 

My research interests are in applying statistical and epidemiological methods to further our understanding of infectious disease transmission, with a particular focus on zoonotic infections and One Health. The ultimate goal of my research is to drive improvements in the health of both humans and animals.

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 fourth year DPhil student in the Department of Statistics, working on building and fitting new models for quantifying meaning change for words in natural languages. I was an undergraduate in Mathematics and Statistics at St Peter's College, Oxford, before going on to work in the industry as a life insurance actuary. After qualifying as a Fellow of the Institute of Actuaries, I returned to Jesus College, Oxford, to read for the MSc in Statistical Science, and stayed on for the DPhil.

Multivariate stochastic processes

Bayesian inference MCMC methods

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 am a mathematician who is currently pursuing a doctoral degree at the Mathematical Institute and the Department of Statistics of the University of Oxford. Within the Mathematical Institute, I am a member of the well-known Center for Doctoral Training in Industrially Focused Mathematical Modelling (InFoMM CDT). During my undergraduate degree, I mainly focussed on pure mathematics and its rigorous methodology. Since then, I have additionally become interested in the applied and data-driven side of the mathematical sciences. I have developed a particular focus on mathematical data science, programming, graphs and networks, deep learning, artificial intelligence and advanced statistical machine learning. In my current DPhil (= PhD) project at the University of Oxford I am collaborating with the research company Lhasa Limited to investigate novel graph-based machine learning techniques and their applications in chemistry and computational drug discovery.

Molecular machine learning techniques have recently shown great promise for important computational drug discovery tasks such as molecular property prediction and activity cliff prediction. The success of such methods, however, crucially depends on the way in which molecular compounds are transformed into informative feature vectors that can be fed into a machine learning pipeline. This is referred to as the problem of molecular representation. In my DPhil project, I am investigating the potential of modern graph-based molecular representation techniques to outperform classical molecular representations such as structural fingerprints and physicochemical descriptor vectors. I am particularly interested in developing novel self-supervised learning strategies for graph neural networks operating on molecular graphs in order to identify and remove hidden performance barriers of state-of-the-art molecular representation methods. The gained insights can be used to design new tailored deep learning architectures for important computational drug discovery tasks such as molecular property prediction and activity cliff prediction.

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