Marcel is a first year student on StatML CDT.  He obtained his B.A. in Natural Sciences at University of Cambridge followed by Harvard University, where he was the Choate Memorial Fellow focusing on data science.

He is also founder of Nural Research, a group exploring AI and grand challenges through a weekly newsletter and research projects.

Causality and robust Bayesian methodology with a particular interest in marrying this to emerging generative architectures and RL. Application focuses are within the field of precision medicine and drug development.

I am a first year StatML student. Prior to this, I completed my Master's in Artificial Intelligence at Imperial College London where I focused on the use of variational inference for model selection in neural networks for my dissertation.

I am particularly interested in robust deep learning, robustness to distribution shift and data-efficient learning (e.g. semi-supervised learning, self-supervised learning).

I am a first year DPhil student working in the (very) broad field of mathematical modelling of infectious diseases. My project is focused on investigating the role that heterogeneity (in all its many forms) plays in the spread of infectious diseases and how we can better incorporate these properties into mathematical and statistical models to enhance evidenced-based disease mitigation policies.  I am especially interested in pursuing an approach that combines graph theory (specifically complex network theory) and contact tracing data. After completing my BA in Maths at the Courant Institute at NYU in 2020, I earned an MSc in Mathematical Modelling and Scientific Computing from the Mathematical Institute (2021) and another in Modelling for Global Health from the Nuffield Department of Medicine (2023), where my dissertation extended a methodological approach to estimating mosquito abundance using climate data.

• Behavioral epidemiology
• Complex network theory
• Heterogeneity in infectious disease models
• Statistical inference from contact tracing and mobility data
• Real-time analysis and nowcasting of infection dynamics

I am currently a Glasstone Fellow at the department of statistics. I was mainly educated in France and I have a mixed academic background in life science and mathematics. I first graduated from the École Normale Supérieure with a major in life science, after which I obtained a PhD in probability theory from Sorbonne Université. My advisors were Amaury Lambert and Emmanuel Schertzer, and I was based at the Center for Interdisciplinary Research in Biology at the Collège de France. Before joining the department, I also did a one-year postdoc at the UQÀM in Montréal on epidemic modeling.

My research lies at the interface of probability theory and population biology. I study probabilistic objects arising from biologically motivated questions

I am both interested in the mathematical structure of these objects and in their application to understanding the underlying biological phenomena. Areas of interest:

• branching processes
• exchangeable coalescents
• random trees / random metric spaces
• population genetic aspects of recombination
• genetics of range expansion
• age-structured models in epidemiology

I am a mathematician and a statistician with a strong interest in biological systems, and I develop computational methods that help to uncover biological and epidemiological knowledge.

I direct Oxford's Schmidt AI in Science programme — a postdoctoral Fellowship programme where Fellows apply methods from AI to advance scientific knowledge.

I am a passionate communicator of statistical ideas (for example, through my YouTube videos), and I develop open-source materials which allow other researchers to understand cutting-edge methods.

Bayesian statistics, mathematical biology, epidemiology, Bayesian nonparametrics, research software

I joined the Statistics Department in September 2023 to work with David Steinsaltz on the development of new methods to study patients with multiple long-term conditions (“multi-morbidity”). The topic is closely related to my recent fellowship work that explored new ways to characterise and classify diseases (CTSU, Oxford Population Health, 2019-22), that was funded by the Nuffield Department of Population Health (NDPH). The fellowship work aimed to develop novel but rigorous methods for big-data epidemiological studies.  

I originally trained in theoretical physics with a Ph.D. on the ageing and stability of emulsions and foams supervised by Prof. Mike Cates at the University of Edinburgh. I joined UKAEA at Culham Science Centre in September 2000, and spent 15 years as a theoretical physicist studying plasma stability in nuclear fusion experiments. Following an M.Sc. in Applied Statistics at the University of Oxford in 2016, I joined NDPH, and my research was predominately based within Oxford University's Big Data Institute until September 2023. In addition to my fellowship project, I have worked on cancer epidemiology (CEU, 2016-2019) and statistical genetics (GSK-funded, 2022-2023). 

My main research interests involve the development and use of novel mathematical methods to utilise the information in large epidemiological datasets. My work has aimed to combine the best existing epidemiological methods with modern clustering techniques and biologically-motivated mathematical models of disease. This has included extensive multi-stage modelling of disease using UK Biobank data (21st Armitage Workshop: Talk by Dr Anthony Webster), and pioneering the use of the Poisson-Binomial model to quantify how much disease-risk can be explained by old age and established risk factors. This latter work provides a first attempt to quantify how much prior diseases are increasing future disease-risk in the UK Biobank cohort, above what is expected based on age and well-known risk factors. My previous research has ranged from theoretical results in soft condensed matter and plasma physics, to cancer epidemiology and climate change. Broadly, my interests lie in the development and use of mathematical and statistical models for problems with social or public health value.