MPhil/PhD Statistical Science
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The Statistical Science research programme at UCL aims to develop research students who can eventually make original contributions to the subject. Students are initially registered for the MPhil degree. No sooner than one year, they are transferred to the PhD degree with retrospective effect if they show a capacity for original work. The typical length of the PhD programme is three years for full-time students and five years for part-time students; an MPhil might be achievable in less.
The admissions process for the MPhil/PhD in Statistical Science operates on a rolling basis, with no fixed deadline for applications. Candidates should apply at least two months in advance of their intended start date.
Entry Requirements
The MPhil/PhD is accessible to students with, or expecting to achieve, a minimum of an upper second-class UK Bachelor’s degree, or a UK Master’s degree in statistics, mathematics, computer science or a related quantitative discipline. Overseas qualifications of an equivalent standard are also acceptable.
In addition to the academic requirements above, all students whose first language is not English must be able to provide recent evidence that their spoken and written command of the English language is adequate. For the MPhil/PhD in Statistical Science, applicants much reach at least the UCL standard level. Further information on this requirement is available at the link below.
Research Areas and Supervisors
In applying for admission to the MPhil/PhD programme, candidates are expected to prepare an outline proposal of their work. This is crucial in identifying potential supervisors. Thus, candidates should peruse the research interests of staff before applying. A list of staff members currently accepting applications for PhD supervision is given below, including an indication of their current research interests and a link to their personal webpage.
It may be helpful to contact a potential supervisor before submitting a formal application. For more information on how to contact potential supervisors and write a research proposal please see UCL’s guidance document. Applications on which no potential supervisor has been specified will still receive consideration, however, in such cases it would be especially important to demonstrate in your reasons for applying that your academic interests align with the Department’s active research areas.
| Researcher | Research Interest Keywords | |
|---|---|---|
| Gareth Ambler | Medical statistics, formulation and validation of risk prediction models, methods to handle missing data, hierarchical models, clinical trials | |
| Gianluca Baio | Bayesian statistical modelling for cost effectiveness analysis and decision-making problems in the health systems, hierarchical/multilevel models and causal inference using the decision-theoretic approach | |
| Julie Barber | Medical statistics, randomised trials and large epidemiological studies, statistical issues in design and analysis of trials | |
| Dr Alessandro Barp | Computational Statistics, General Theory and Methodology, Biostatistics | |
| Tom Bartlett | Statistical genomics and more generally statistics for cell biology (N.B. not population genetics), sparse multivariate models (frequentist or Bayesian), stochastic networks | |
| Alexandros Beskos | Sequential Monte-Carlo, Markov chain Monte-Carlo, Bayesian statistics, computational statistics, Monte-Carlo algorithms in high-dimensions, inverse problems, inference, applications and simulation for stochastic differential equations, fractional and white noise in econometrics, hidden Markov models, biostatistics | |
| François-Xavier Briol | Computational statistics, Monte Carlo methods, kernel methods, machine learning, statistical emulators, Gaussian processes | |
| Richard Chandler | Environmental applications, climate projections, uncertainty analysis, space-time modelling | |
| Codina Cotar | Probability theory applied to physics and biology, optimal transport theory, statistical mechanics | |
| Petros Dellaportas | Machine learning, Bayesian statistics | |
| Jim Griffin | Bayesian statistics, regression, time series, computational methods for Bayesian inference, high-dimensional and nonparametric statistics, bioinformatics, applications: economics, finance, ecology, the environment, and sport science | |
| Serge Guillas | Uncertainty quantification of computer models, functional data, time series, high-dimensional statistics, environmental statistics | |
| Jeremias Knoblauch | Machine learning, robustness, Bayesian inference, Generalised Bayesian methodology, variational methods, time series | |
| Brieuc Lehmann | Uncertainty quantification of computer models, functional data, time series, high-dimensional statistics, environmental statistics | |
| Baptiste Leurent | Medical statistics, missing data, multiple imputation, clinical trials, cluster-randomised trials, health economics | |
| Samuel Livingstone | Bayesian computation, Monte Carlo, Markov chains, encrypted statistics | |
| Sebastian Maier | Computational stochastic optimisation, Approximate Dynamic programming/Reinforcement learning, Decision-making under uncertainty, Operational research | |
| Ioanna Manolopoulou | Bayesian statistics, semi- and non-parametric modelling, mixture modelling, state-space models, health data science, heterogeneous data | |
| Giampiero Marra | Penalized likelihood based inference, copula regression modelling, generalized additive modelling, endogeneity, non-random sample selection, observed and unobserved confounding, generalized regression, computational statistics, parametric and nonparametric survival modelling, simultaneous equation modelling, applications in various areas | |
| Paul Northrop | Extreme value modelling; statistical methods for the environmental sciences, e.g. off-shore engineering, climate science and hydrology | |
| Rumana Omar | Medical statistics, biostatistics, missing data, clustered data (e.g. multicentre studies, repeated measurement studies), risk prediction models, trial (not early phase drug trials) methodology | |
| Menelaos Pavlou | Risk prediction modelling, analysis of clustered data, informative cluster size, missing data, penalised regression, methods for comparing institutional performance. | |
| Yvo Pokern | Stochastic differential equations, Gaussian Markov random fields, Bayesian inverse problems | |
| Javier Rubio | Bayesian Statistics; Model Selection; Survival Models; Longitudinal Models; Biostatistics; Computational Statistics | |
| Kayvan Sadeghi | Graphical models, random network modelling, social networks, causal inference | |
| Ricardo Silva | Causal inference, variational methods, graphical models, Bayesian inference | |
| Emma Simpson | Extreme value analysis, focused on dependence modelling in multivariate, spatial and spatio-temporal settings and environmental applications. | |
| Terry Soo | Probability theory, ergodic theory | |
| Katerina Stavrianaki | Flood risk, multi-hazard risk assessments, statistical seismology, stochastic modelling, seismic hazard and rock mechanics | |
| Ardo van den Hout | Methods for longitudinal data, multi-state models, joint models, mixed-effects models, spline models, biostatistics, medical statistics | |
| Alexander Watson | Lévy processes and applications, optimal control and stopping problems, models of fragmentation and growth, branching processes. | |
| Hilde Wilkinson-Herbots | Stochastic models in genetics | |
| Jinghao Xue | Statistical machine learning, multivariate and high-dimensional data analysis, statistical classification, pattern recognition and image analysis |
Curriculum
Unlike the taught Statistics MSc programme, the MPhil/PhD has no required curriculum. However, students are expected to agree on a customised programme of study with their supervisor, which may involve specialisation courses (either at UCL or at the London Taught Course Centre) or independent reading. Attendance at research seminars is encouraged, and students who have been upgraded to PhD status are required to present their research in a separate seminar stream once per year. Finally, the UCL Graduate School has its own requirements for training courses.
Funding
Some departmental funding is usually available. UCL also offers a number of scholarships and other funding for UK, EU and overseas students undertaking research studies at the University. Further information, including eligibility criteria and application deadlines, can be found at the links below.
Contact Details
For more information on the programme please contact:
Ms Marina Lewis
stats.pgr-admissions AT ucl.ac.uk
+44 (0)20 7679 1868
Please note that all professional services staff are currently working away from the office and are therefore unable to take phone calls on the number above.