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MPhil/PhD Statistical Science

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.

ResearcherResearch Interest Keywords 
Gareth AmblerMedical statistics, formulation and validation of risk prediction models, methods to handle missing data, hierarchical models, clinical trials 
Gianluca BaioBayesian 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 BarberMedical statistics, randomised trials and large epidemiological studies, statistical issues in design and analysis of trials 
Tom BartlettStatistical genomics and more generally statistics for cell biology (N.B. not population genetics), sparse multivariate models (frequentist or Bayesian), stochastic networks 
Alexandros BeskosSequential 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 BriolComputational statistics, Monte Carlo methods, kernel methods, machine learning, statistical emulators, Gaussian processes 
Richard ChandlerEnvironmental applications, climate projections, uncertainty analysis, space-time modelling 
Codina CotarProbability theory applied to physics and biology, optimal transport theory, statistical mechanics 
Petros DellaportasMachine learning, Bayesian statistics 
Jim GriffinBayesian 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 GuillasUncertainty quantification of computer models, functional data, time series, high-dimensional statistics, environmental statistics 
Baptiste LeurentMedical statistics, missing data, multiple imputation, clinical trials, cluster-randomised trials, health economics 
Samuel LivingstoneBayesian computation, Monte Carlo, Markov chains, encrypted statistics 
Sebastian MaierComputational stochastic optimisation, quantitative risk management, decision making under uncertainty 
Ioanna ManolopoulouBayesian statistics, semi- and non-parametric modelling, mixture modelling, state-space models, health data science, heterogeneous data 
Giampiero MarraPenalized 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 NorthropExtreme value modelling; statistical methods for the environmental sciences, e.g. off-shore engineering, climate science and hydrology 
   
Rumana OmarMedical statistics, biostatistics, missing data, clustered data (e.g. multicentre studies, repeated measurement studies), risk prediction models, trial (not early phase drug trials) methodology 
Menelaos PavlouRisk prediction modelling, analysis of clustered data, informative cluster size, missing data, penalised regression, methods for comparing institutional performance. 
Yvo PokernStochastic differential equations, Gaussian Markov random fields, Bayesian inverse problems 
Kayvan SadeghiGraphical models, random network modelling, social networks, causal inference 
Ricardo SilvaCausal inference, variational methods, graphical models, Bayesian inference 
Terry SooProbability theory, ergodic theory 
Katerina StavrianakiFlood risk, multi-hazard risk assessments, statistical seismology, stochastic modelling, seismic hazard and rock mechanics 
Ardo van den HoutMethods for longitudinal data, multi-state models, joint models, mixed-effects models, spline models, biostatistics, medical statistics 
Alexander WatsonLévy processes and applications, optimal control and stopping problems, models of fragmentation and growth, branching processes. 
Hilde Wilkinson-HerbotsStochastic models in genetics 
Jinghao XueStatistical 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.