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My personal website

• Statistical Machine Learning
• Active Learning
• Bayesian Experimental Design

I am a Postdoctoral Research Associate in the group of Gesine Reinert. I recently completed my PhD at the University of Göttingen under the supervision of Dominic Schuhmacher and Gesine Reinert. My research focusses on the study of spatial random graphs using Stein’s method, kernelized Stein discrepancies and tools from spatial stochastics. I am furthermore interested in differential privacy. 

For additional information, please visit my personal website.

• Spatial random graphs
• Stein’s method
• Goodness-of-fit testing
• Differential privacy 

I am currently a Florence Nightingale Bicentenary Fellow in the Department of Statistics at the University of Oxford. My research lies at the interface of stochastic analysis, mathematical biology, and partial differential equations (PDEs). Much of my recent work has focused on interacting particle systems motivated by models from population genetics, investigating both their scaling limits and the biological interpretation of these limiting behaviours.

• Interacting particle systems and their scaling limits 

• Population genetics

• Mathematical biology

• Partial differential equations (PDEs)

• Statistical Genetics

I am a Florence Nightingale Research Fellow and I develop methods for modelling infectious diseases in resource-limited settings, with an overall objective to advance scientific understanding, inform policy, and benefit global health. 

Throughout this research, a key question is understanding when, where, and why infectious diseases spread. To respond and plan accordingly, decision-makers need such information in a timely and reliable manner. The power of modelling lies in the ability to provide such information, and therefore, I focus on methods that can i) learn from the past, ii) monitor the present, and iii) plan for the future.

Often focusing on climate-sensitive infectious diseases in low- and middle-income countries, I develop interdisciplinary methods which blend theory and knowledge from across maths, statistics, and AI. These methods often integrate heterogeneous models, data, and theory from across branches of science, and are grounded in practical public health objective of providing actionable information for decision-makers. 

Mathematical biology, global health,  mosquito-borne diseases, forecasting and forecast evaluation

• I am currently a postdoc mentored by Prof Gesine Reinert, working on applications of Stein's method and topological data analysis to machine learning within the Erlangen AI Hub on the Mathematical Foundations of AI.

• Previously, I was a postdoc at Cardiff University in the Geometry, Algebra, Physics, and Topology group of the School of Mathematics, mentored by John Harvey between 2023 - 2025. 

• I obtained my DPhil in Mathematics at Oxford in 2022 (supervised by Vidit Nanda and Peter Grindrod CBE), as a part of the Industrially Focused Mathematical Modelling CDT. 

Further information about my research interests and publication list can be found on my personal webpage:

• Research statement
• Publications List
• Talks
• Teaching
   

Topological data analysis (TDA) is about understanding the shape of data. I focus on the following themes within TDA. 

• Morse theory (smooth, algebraic, and topological)
• Machine Learning on Graphs and spectral properties
• Data Analysis on non-Euclidean manifolds
• Estimating Properties of Shapes from Samples

Chris Taylor completed an MSci in Chemistry at the University of Bristol in 2010, and stayed on to achieve a Ph.D in Theoretical Chemistry in 2014 under the supervision of Prof. Fred Manby.  He subsequently took up a series of postdoctoral research roles with Prof. Graeme Day at the University of Southampton, undertaking over a decade of research in the field of organic molecular crystal structure prediction. 

Since 2025, he holds the position of Research Software Engineer in the Department of Statistics, working with Prof. Charlotte Deane and the Oxford Protein Informatics Group to develop, deploy, and maintain software tools to assist the discovery, simulation, and understanding of antibodies and small molecules for therapeutics.

Beyond his software engineering activities within OPIG, his research interests lie in computational chemistry method development and application, simulation of intermolecular interactions, and in silico prediction and discovery of novel molecules and materials.