Research areas for entry in October 2017

FF Manifestation

Additional supervisors/co-supervisors may be available, other than those listed.

Computational Statistics and Statistical Methodology

Supervisor: Professor Arnaud Doucet
Possible research areas: sequential Monte Carlo for high-dimensional filtering, non-reversible Markov chain Monte Carlo, decentralized Markov chain Monte Carlo algorithms, transport-based methods  for Bayesian computation.

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 Chris Holmes
Bayesian statistics, statistical machine learning, decision theory, scalable models, biomedical applications.

Supervisor: Professor Jonathan Marchini
Statistical and population genetics; Imaging-genetics; Bayesian methods

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
Scalable inference, learning, and optimization in high-dimensional models. Design and analysis of algorithms in Machine Learning, with applications to graphical models and Monte Carlo methods.

Supervisor: Dr Marco Scutari
Bayesian networks: network structure learning in high-dimensional settings or with Big Data; learning and inference for complex networks with hybrid and missing data; high-performance software implementations; applications to sequence data from statistical genetics.

Supervisor: Professor David Steinsaltz
Survival analysis and clinical trials, including Bayesian methods and meta-analysis. Longitudinal and genetic data in medical and sociological contexts.

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


Probability

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

Supervisor: Professor Paul Chleboun
Interacting particle systems and applications in models inspired by physics (non-equilibrium statistical mechanics). In particular, models of condensing systems and amorphous materials. Large deviations and equivalence of ensembles. Critical phenomena and metastability.

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, including interacting particle systems, models of random growth and percolation, and processes of coagulation and fragmentation

Supervisor: Professor Gesine Reinert
My main research interest is the 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: Dr Matthias Winkel
Probability and stochastic processes, in particular problems involving branching processes, Levy processes, fragmentation processes, random tree structures.

Protein Informatics

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.

Statistical Genetics and Bioinformatics

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 Jonathan Marchini
Statistical and population genetics; Imaging-genetics; Bayesian methods

Supervisor: Dr Marco Scutari
Bayesian networks: network structure learning in high-dimensional settings or with Big Data; learning and inference for complex networks with hybrid and missing data; high-performance software implementations; applications to sequence data from statistical genetics.

If you are interested in biomedical applications of statistics,  the Systems Approaches to Biomedical Sciences CDT or the Systems Biology CDT offer fully funded places to study in the Department.