I am a postdoctoral research assistant in computational statistics and machine learning at the University of Oxford supervised by Arnaud Doucet and funded by the CoSInES project. I recently have joined the algorithms and inference working group in Next Generation Event Horizon Telescope (ngEHT) collaboration to help improve the algorithms used to model and image supermassive black holes. Before this, I completed a PhD in Statistics with Alexandre Bouchard-Côté at the University of British Columbia.

Monte Carlo methods, scalable Bayesian inference, parallel tempering, sequential Monte Carlo, information geometry, statistical physics.

I am a computational biologist developing methods for structural biology, primarily cryo-EM for drug discovery.

I hold a BSc degree in Biotechnology, another one in Computer Science and an MSc in Biophysics. I did my PhD at the Spanish National Center of Biotechnology (CSIC) and the Autonomous University of Madrid (UAM), under the supervision of Professor Jose Maria Carazo and Dr Joan Segura. During that time, I developed machine learning algorithms for structural biology, including BIPSPI and DeepEMhancer.

I joined the Department of Statistics in 2020 where, in collaboration with XChem, I worked on AI algorithms for fragment-based drug discovery for almost two years. Currently, I am trying to improve cryo-EM algorithms to facilitate drug discovery.

• Machine Learning / Deep Learning 
• Structural biology  
• Cryo-EM 
• Drug discovery 

I'm currently working in the Department as a Florence Nightingale Bicentennial Fellow. I previously completed a postdoc with Chris Holmes and Arnaud Doucet, working on causal inference and conformal prediction. Before that I completed my DPhil as part of the AIMS CDT under the supervision of Arnaud Doucet and George Deligiannidis. My thesis covered several topics in (primarily Bayesian) computational statistics and machine learning, including Monte Carlo methods and deep generative modelling. Before they left Oxford, I also worked with Frank Wood and Hongseok Yang on probabilistic programming.

I am broadly interested in ensuring the robustness of complex, safety-critical systems. I have worked on techniques for reliable uncertainty quantification, causal inference, as well as a variety of topics across machine learning. An underlying focus of my research is on methodology that is valid under minimal assumptions, which allows their application in large-scale real-world settings where more specific assumptions may be difficult to justify.

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.