Supervisor: Professor François Caron
Bayesian Statistics, Statistical Machine Learning, Statistical Network Analysis, Bayesian Nonparametrics.

Supervisor: Professor Mihai Cucuringu
Development and mathematical & statistical analysis of algorithms that extract information from massive noisy data sets. Computationally-hard inverse problems on large graphs with applications in machine learning. Spectral and semidefinite programming algorithms with application to ranking, clustering, group synchronization, phase unwrapping. Network analysis: community and core-periphery structure, network time series. Statistical analysis of financial data, statistical arbitrage, limit order books, risk models.

Supervisor: Professor George Deligiannidis
Computational statistics, in particular theory and methodology for Monte Carlo methods, especially MCMC and SMC for high-dimensional targets; limit theorems and convergence rates for Markov chains and stochastic processes in general; random walks.

Supervisor: Professor Arnaud Doucet
Possible research areas: Bayesian Computation, Monte Carlo methods, Statistical Machine Learning.

Supervisor: Professor Judith Rousseau
Bayesian Statistics, Bayesian nonparametrics,  complex and high dimensional models, mixture models, networks, asymptotic theory for Bayesian methods, model selection and tests, prior elicitation, Bayesian computations and approximate Bayesian computation, manifold  learning.

Supervisor: Professor Yee Whye Teh
Machine learning. Probabilistic modelling, learning and inference.

Supervisor: Professor Mihai Cucuringu
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 statistical arbitrage, machine-learning for asset pricing, lead-lag detection, market microstructure, limit order books, synthetic data generation, as well as nonlinear dimensionality reduction techniques for high-dimensional time series data.

Supervisor: Professor Frank Windmeijer
Causal Inference, Instrumental Variables Estimation (instrument selection using machine learning, weak instrument robust inference, bootstrap), Mendelian Randomisation.

Econometrics and Population Statistics

Supervisor: Professor Julien Berestycki
Branching processes, branching random walks, coalescence, fragmentation, population genetics, reaction-diffusion equations, front propagation, random trees.

Supervisor: Professor Alison Etheridge
Stochastic analysis, especially problems related to stochastic modelling in population genetics.

Supervisor: Professor Christina Goldschmidt
Research area: random discrete structures (eg trees and graphs) and their scaling limits.

Supervisor: Professor James Martin
Probability theory, with strong links to statistical physics and theoretical computer science. Particular interests include: random graphs; interacting particle systems; models of random growth and percolation; models of coagulation and fragmentation; queueing networks.

Supervisor: Professor Gesine Reinert
Investigation of networks such as protein-protein interaction networks and social networks in a statistically rigorous fashion. Often this will require some approximation, and approximations in statistics are another of my research interests. There is an excellent method to derive distances between the distributions of random quantities, namely Stein’s method, and I am interested in Stein’s method also from a theoretical viewpoint. The general area of my research falls under the category Applied Probability and many of the problems and examples I study are from the area of Computational Biology.

Supervisor: Professor David Steinsaltz
Random dynamical systems, particularly with applications to population ecology. Evolutionary and biodemographic models of ageing.

Supervisor: Professor Matthias Winkel
Probability and stochastic processes, in particular problems involving branching processes, Levy processes, fragmentation processes, random tree structures.

Probability

Supervisor: Professor Charlotte Deane
Developing novel methodologies to understand and predict protein evolution, interaction, structure and function.

Supervisor: Professor Garrett Morris
Developing novel therapeutics and improving our understanding of living systems at the molecular level, in particular methods development in computer-aided drug discovery. Harnessing the increasing amounts of experimental data, and the development of novel algorithms in chemoinformatics and bioinformatics, machine learning, network pharmacology, and structural biology, to help solve real-world drug discovery problems.

Oxford Protein Informatics Group

Supervisor: Professor Robert Davies
Statistical and population genomics (low coverage sequencing, imputation, large datasets, MCMC, Gibbs sampling), statistical methods development for precision medicine (polygenic risk scores, clinical utility, non-invasive prenatal testing).

Supervisor: Professor Christl Donnelly
Epidemiology of infectious disease; Real-time analysis of outbreaks; Biostatistics; Disease ecology; Applied statistics.

Supervisor: Professor Jotun Hein
Algorithms in Bioinformatics, Computational Biology, Stochastic Models of Genealogies and Sequence Evolution, Mathematical Models of the Origin of Life, Stochastic Models of Network Evolution, Genome Analysis

Supervisor: Professor Simon Myers
Statistical and population genomics (fine-scale population structure and migrations, recombination, natural selection on complex traits, association testing, demographic history), statistical approaches for single-cell data (RNA-seq, ATAC-seq), genetic determinants of speciation and fertility in mammals.

Supervisor: Professor Pier Palamara
Computational methods for population genetics (natural selection, demographic history); statistical genetics (complex trait heritability, association); scalable methods for large genomic data sets.

Statistical Genetics and Epidemiology

Supervisor: Professor Robin Evans
Graphical models; Causal inference; Marginal modelling; Combining causal information from different experimental settings; Confounding and selection bias; High-dimensional model selection, and low dimensional model selection in the presence of high-dimensional confounders.

Supervisor: Professor Geoff Nicholls
Applied Bayesian Statistics and Statistical Methods, focusing on building and fitting models for complex stochastic systems.
Computational Statistics, in particular Monte Carlo Algorithms. Current projects: Multiple imputation and model misspecification; Monte Carlo filtering and inference for partial orders from rank data; Spatial Statistics and the location of texts; Phylogenetic inference for cultural traits.

Supervisor: Professor Patrick Rebeschini
Investigation of fundamental principles in high-dimensional probability, statistics and optimisation to design computationally efficient and statistically optimal algorithms for machine learning.

Supervisor: Professor Judith Rousseau
Bayesian Statistics, Bayesian nonparametrics,  complex and high dimensional models, mixture models, networks, asymptotic theory for Bayesian methods, model selection and tests, prior elicitation, Bayesian computations and approximate Bayesian computation, manifold  learning.

Statistical Theory and Methodology