Hi! I'm Olly a Junior Research Fellow at New College. I previosly did my PhD in Biochemistry in Cambridge with Kathryn Lilley and an MMath at Warwick. My work is primarily Bayesian computational methods to understand data rising from biologically motivated problems.

I develop statistical and machine learning methods for biochemical and biophysical data. My work spans both systems and structural biology with a focus on understanding how proteins function through the lens of computation. I make paritcular use of Bayesian non-parametrics and machine/deep learning.

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 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