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 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.