Welcome to Econometrics and Population Statistics
The Econometrics and Population Statistics group develops and deploys statistical and mathematical methods to resolve questions that arise from the quantitative study of populations, markets and interventions. These include the exploration of social, economic, financial, ecological, and population-level health phenomena.
Algorithms and data science
The development and mathematical/statistical analysis of algorithms that extract information from high-dimensional noisy data sets, network time series, and certain computationally-hard inverse problems on large graphs. Particular areas of focus include the statistical analysis of big financial data, statistical arbitrage, market microstructure, limit order books, synthetic data generation, as well as nonlinear dimensionality reduction techniques for high-dimensional time series data. (Lead: Mihai Cucuringu)
Causal Inference
Causal inference, in particular the identification and estimation of causal effects in situations where standard estimation techniques are invalid due to the presence of unobserved confounders. Research focuses on Instrumental Variables estimation, in particular testing for underidentification, weak instruments and the performance of weak instruments robust inference, and selection of valid instruments, incorporating machine learning techniques. These methods are applied across many fields of study, including biostatistics, epidemiology, where Mendelian Randomisation studies use genetic markers as instrumental variables for modifiable phenotypes, social sciences and asset pricing models in finance. (Lead: Frank Windmeijer)
Population Statistics
The group is actively involved in exploring statistical and mathematical problems that arise from the study of biological populations. Much of this work is devoted in particular to questions of biological ageing, including problems of mathematical evolutionary theory, mathematical ecology, methods for longitudinal social and medical data, and survival analysis. This work implicates a broad range of technical areas, from stochastic dynamical systems through Bayesian computation to kernel methods and deep learning, and applies these tools to relevant data that will help to illuminate fundamental issues about life history in humans and other organisms. (Lead: David Steinsaltz)
Join us for doctoral study
Welcome to Oxford Probability
Oxford has a large and thriving community of researchers in probability, spanning the Department of Statistics and the Mathematical Institute. Within the Department of Statistics, our research interests include interacting particle systems, random trees and branching processes, random graphs and networks, percolation, mathematical population genetics, stochastic analysis and Stein’s method.
We work closely with the Stochastic Analysis group in the Mathematical Institute.
Our People
The Probability group includes 7 permanent faculty, around 15 doctoral students and a few postdocs .
Events
We organise a weekly Probability Seminar. We are also involved in the online Oxford Discrete Mathematics and Probability Seminar, which currently takes place a couple of times a term, and Oxford Research on Probability and Machine Learning which runs termly seminars.
Keep in Touch
You can stay informed about what we do by subscribing to the Oxford Probability Mailing list.
Join us for doctoral study
We take both standard DPhil students and DPhil students through the EPSRC CDT in the Mathematics of Random Systems which is run jointly between the Mathematical Institute, the Department of Statistics and the Department of Mathematics at Imperial College.
Senior Statistical Consultant
Biographical Sketch
I am a biostatistician and evolutionary biologist. Prior to taking a position as a consultant, I was a researcher in the Department of Statistics at the University of Oxford as a member of Prof David Steinsaltz's group in biodemography. I am an expert in quantitative biology with extensive experience in teaching and training. My expertise ranges from experimental design to machine learning, with specialised knowledge in data handling of large longitudinal data. My background as a researcher has given me the experience necessary to support grant applications and manuscript writing, as well as ensure that good statistical practices are embedded in research projects. I am particularly passionate about developing and delivering training in statistics and R for non-statisticians.
2022- present: Senior Statistical Consultant, Oxford University Statistical Consulting
2018-2022: Postdoctoral Researcher in Biostatistics, University of Oxford
2016-2018: Postdoctoral Researcher in Sociogenomics, University of Oxford
2012-2016: PhD in Biological Sciences, University of Reading
2009-2010: MSc Plant Diversity - Taxonomy and Evolution, University of Reading
2006-2009: BSc(Hons) Biological Sciences, Imperial College London
2003-2006: BSc(Hons) Mathematics with Statistics, Imperial College London
Publications
Contact Details
Email: maria.christodoulou@stats.ox.ac.uk
Office: 3.10
Pronouns: She/Her/Hers
Mental Health First Aider
Research Groups
Students
News
Welcome to OxUSC
Oxford University Statistical Consulting (OxUSC) is a source of statistical advice and expertise for clients from businesses and organisations and for researchers and staff at the University of Oxford.
We work closely with our clients across disciplines and sectors to answer questions relating to study design, data collection, analysis and visualisation of results. We also offer statistical training tailored to your context and needs.
Internal clients
If you are a University of Oxford DPhil, researcher/academic or staff member and need support with the use of statistical methods in your research or day-to-day operations, we can advise and provide expertise across all stages of your project.
External clients
We work with you to refine your questions, help you get the most out of your data, draw the correct conclusions from your analysis, and communicate your results.
Case studies
Read our case studies to learn more about the types of projects we have been recently involved in.
Professor of Biostatistics
Biographical Sketch
I moved to Oxford from Imperial College London in February 2004. At Imperial College I studied for my doctorate in Bayesian statistics, investigating novel nonlinear pattern recognition methods. This was followed by a post-doctoral position and then a lectureship at Imperial. Previous to this I worked in industry for a number of years researching in scientific computing, developing techniques for real-time pattern recognition models in defence and SCADA (Supervisory Control and Data Acquisition) systems. My current research is focussed on applications and statistical methods development in the genomic sciences and genetic epidemiology. I hold a Programme Leaders Grant in Statistical Genomics from the Medical Research Council.
Research Interests
- Bayesian statistics
- Stochastic simulation
- Markov chain Monte Carlo
- Pattern recognition
- Spatial statistics
- Statistical genetics
- Statistical genomics
- Genetic epidemiology
I have a broad interest in the theory, methods and applications of statistics and statistical modelling. My background and beliefs lie in Bayesian statistics which provides a unified framework to stochastic modelling and information processing. I am particularly interested in pattern recognition and nonlinear, nonparametric methods.
Publications
Contact Details
College Affiliation: Fellow at St Anne's College
Email: cholmes@stats.ox.ac.uk
Telephone: +44(0)1865 285874
Office number: 1.08
Associate Professor of Probability
Biographical Sketch
I was educated in France where I graduated jointly from ENSAE (National School for Statistics and Economic Administration) and Université Paris VI in 2000. After my PhD in Paris VI (2003) in Probability, I was Maitre de Conférences in Marseille for three years and then in Paris until 2014 when I joined the Department of Statistics and Magdalen College in Oxford.
During this time I have also twice been visiting professor at NYU-Abu Dhabi as well as a long term visitor at the University of Bath.
Research Interests
- Branching processes
- Branching random walks
- Coalescence
- Fragmentation
- Population genetics
- Reaction-diffusion equations
- Front propigation
- Random trees
My research is in probability theory and focuses essentially on models and situations which involve tree-like structures and branching phenomena. Examples include coalescent processes, branching processes, continuous random trees, branching random walks… These models are not only endowed with a remarkably rich mathematical structure that connects them to many area of mathematics, but they also occur naturally in physical sciences, in population genetics and in biology. Questions that arise in these fields are a major motivation of my work.
Publications
Contact Details
College Affiliation: Tutorial Fellow at Magdalen College
Email: julien.berestycki@stats.ox.ac.uk
Telephone: +44(0)1865 281881
Office number: 3.09
Graduate Students
David Geldbach
Research Groups
Professor of Probability
Biographical Sketch
2003-Present - Professor of Probability, University of Oxford
2024-Present - President of the Academy for the Mathematical Sciences
2019-2022 - Head of Department, Department of Statistics, University of Oxford
1999-2005 - EPSRC Advanced Fellow, University of Oxford
1997-2012 - University Lecturer in Applied Mathematics, University of Oxford, in association with a Tutorial Fellowship at Magdalen College
1996-1997 - Reader in Probability and Statistics, Queen Mary and Westfield College, University of London
1992 - Neyman Assistant Professor, Department of Statistics, University of California at Berkeley
1990-1996 - Lecturer in Pure Mathematics, University of Edinburgh
Research Interests
I began graduate work as a student in functional analysis and rapidly became interested in the interface between probability and analysis. Much of my work focuses on infinite dimensional stochastic processes and their applications. Most recently my central interest has been a collection of mathematical problems arising in population genetics.
Publications
Contact Details
College Affiliation: Fellow by Special Election at Magdalen College
Telephone: +44(0)1865 281244
Office number: 3.07