I’m currently a postdoctoral researcher in immunoinformatics working with Professor Charlotte Deane in the Oxford Protein Informatics Group.

2021-Present: Postdoctoral Researcher in Immunoinformatics, Oxford Protein Informatics Group
2020-2021: Postdoctoral Research Assistant in Immunoinformatics, Oxford Protein Informatics Group
2016-2020: DPhil in Immunoinformatics, Oxford Protein Informatics Group (New College)
2012-2016: MChem in Chemistry (University of Oxford, Merton College)

Analysing datasets of B-cell/antibody/T-cell sequences and structures to better characterise the adaptive immune system, improving our understanding of pathogen responses, immunosenesence, immunodeficiency, autoimmunity, allergy, and cancer. I’m particularly motivated to translate the lessons we learn to the design of novel diagnostics and therapeutics that improve patient outcomes.

I started my academic adventure with mathematical physics at the University of Edinburgh, eventually focusing on statistical physics and mathematical modelling of biological systems. Afterwards, I hit the snooze button on the alarm clock of life and did my DPhil in Systems Biology here in Oxford, in which I focused on applying machine learning to protein-ligand interactions. Now I'm a research software engineer in the Oxford Protein Informatics Group, where I focus on developing research projects into robust software that can be deployed to production both in our group and to the wider world through web applications and commercially-licenced software packages.

My own research interests lie in the realm of small molecule drug discovery, in particular predicting protein-ligand binding. In practice, most of my time is spent developing immunoinformatics tools and web applications in collaboration with the rest of the Oxford Protein Informatics Group.

I am an Associate Professor of Statistical Machine Learning, leader of the RainML Research Lab (rainml.uk), Tutorial Fellow at Mansfield College, and Principal Investigator of the ERC Starting Grant Data-Driven Algorithms for Data Acquisition (Mar 2024 - Feb 2029, funded by the UKRI Horizon Guarantee Scheme).  Though I only started my current Associate Professor role in September 2024, I have been a member of the Department since 2017, first as a postdoc working with Yee Whye Teh (Sep 2017 - Aug 2019), then as an associate member as part of a Junior Research Fellow in Computer Science at Christ Church College (Sep 2019 - Dec 2019), then as a Florence Nightingale Bicentennial Fellow and Tutor in Statistics and Probability (Jan 2020 - Feb 2024), and finally a Senior Research Fellow supported by my own ERC grant (Mar 2024 - Aug 2024). I originally studied Mechanical Engineering (MEng) at the University of Cambridge, while I did my D.Phil in Oxford under the supervision of Frank Wood and Michael Osborne, focusing mostly on probabilistic programming and Monte Carlo methods.  I also had a stint working in the Ferrari Formula 1 team in between the two.

Personal website: https://www.robots.ox.ac.uk/~twgr/

My research covers a wide range of topics in and around statistical machine learning and experimental design, with areas of particular interest including: 

• Bayesian experimental design
• Probabilistic and data-efficient approaches to machine learning
• Active learning
• Deep learning, with a particular focus on probabilistic approaches, deep representation learning, and deep generative models
• Probabilistic programming
• Approximate inference and Monte Carlo methods

Please see my Google Scholar page for an up-to-date list of publications.

I received my PhD in Biostatistics from the University of Toronto, Canada in 2017, specializing in methods for analysis of microbiome and ecological community data. Subsequently, I worked as a biostatistician in arthritis research, conducting analyses of metabolomic and miRNA signature data, and in population health, where I estimated temporal effects of polygenic risk scores in obesity. Currently I'm the Analytics Lead for the IL-17 project group within the Oxford-Novartis collaboration.

My research focus is on methods development with applications to clinical and biomedical data, particularly methods for addressing sparsity. Some of my previous work includes inference methods for rare species in microbiome data and total species estimators. My current work is using random effect splines for modelling and prediction in longitudinal data.

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