Six PhD studentships in Statistical Methodology and Its Application at University College London

The studentships are attached to the Department of Statistical Science at
University College London, and a subset of them are UCL Impact awards.
Impact awards support collaborative studentship projects with
organisations such as charities, companies, government institutions and
social enterprises. The impact awards are joint with Lloyds bank, Xerox
Research Centre Europe (, and NCR Labs, respectively.

UCL is a member of the London Taught Course Centre ( that
provides additional training in foundations of Mathematics and Statistics.
UCL also offers training via its graduate school.

UCL is among the top-ten research institutions in the world and the
Department of Statistical Science is one of the three largest statistics
groups in the UK having a unique combined strength in Statistical
Methodology and Machine Learning. The studentships are based in the
Department of Statistical Science which has over twenty full time members
of staff, including Professors Tom Fearn, Mark Girolami, Valerie Isham,
Sofia Olhede and Trevor Sweeting. Together with other groups at UCL the
department forms the Centre for Computational Statistics and Machine
Learning (CSML) (, which is part of the European
Network of Excellence PASCAL (

For informal inquiries please contact Professor Mark Girolami
girolami(at) or Professor Sofia Olhede, sofia(at)
Candidates should complete the general UCL PhD application form, available

1. Advanced Monte Carlo Methods for Images and Text (with Xerox) – Prof.
The Bayesian framework for statistical inference is largely dependent on
numerical simulation for all but the most straightforward of statistical
models. In the probabilistic representation of digital documents comprised
of texts, images and embedded information, sophisticated statistical
models are often required. It is hugely challenging to perform simulation
based inference over these classes of models due to a variety of factors
such as (1) exceedingly high number of parameters in the model, (2) the
discrete nature of the configuration space, (3) lack of strong
likelihood-based identifiability and (4) strong posterior correlation of
parameters. This project will seek to develop generic Monte Carlo
sampling methods that addresses some of the issues listed above. The
research will be carried out in close collaboration with Dr Cedric
Archambeau ( and Dr Guillaume Bouchard
( The successful candidate will have the
opportunity to visit Xerox Research Centre Europe ( on
a regular basis.

2. Evolving Lead and Lag Times of Credit Cycles (with Lloyds) – Prof. S.
For policymakers and companies constructing accurate business and credit
cycle indicators is pivotal for future planning, as well as for managing
risk and troughs in cycles. Such indicators are constructed from
observations of multiple time series, such as gross domestic product,
production for certain sectors, employment, spread of interest rates, that
is from collections of multiple observations of many different processes
at several time instances. It is particularly important to identify
leading credit cycle indicators in such data, as these will show effects
of a changing financial climate ahead of other variables changing and thus
they will allow for prior warning of a worsening or improving climate.
This studentship will develop time series methods to estimate leads and
lags, as well as the common cyclical structure in multiple time series, in
particular accounting for evolving structure in the relationships to
identify evolving leading indicators.

3. Geometric Markov chain Monte Carlo – Prof. M.Girolami
A recent paper read before the Royal Statistical Society developed
sampling methodology based on Riemannian geometric principles and provided
a way forward in systematically addressing some of the biggest challenges
faced in modern day computational statistics. The ability to design
proposal mechanisms for Markov chain Monte Carlo (MCMC) that traverse
geodesics and transform in a covariant manner across the statistical
manifold brings great potential to what problems can conceivably be
addressed. In this project the student will work on the further
development and analysis of this methodology from a number of possible
perspectives such as considering alternative geometries.

4. Probabilistic Models for Adaptive Content Creation (with Xerox) – Prof.
This research project will focus on the development of structured
prediction models to build document templates and learn to customize texts
or sentences according to user preferences and habits. Conditional
language models to generate human readable text based on the specific
target application and device appropriate algorithms for the generation of
small pieces of text, such as introductory sentences will also be
developed. This project will draw upon recent advances in Natural Language
Processing tools, Machine Learning algorithms and Stochastic Optimization
techniques, in developing intelligent document creation tools. The
research will be carried out in close collaboration with Dr Cedric
Archambeau ( and Dr Guillaume Bouchard
( The successful candidate will have the
opportunity to visit Xerox Research Centre Europe ( on
a regular basis.

5. Spatio-Temporal Statistical Models of Banknote Ageing (with NCR) –
Prof. M.Girolami
A practical challenge to fully realising automated currency validation in
Automated Teller Machines (ATM) is the variable quality of banknotes
presented to the machine. It is desirable that a probabilistic generative
model, and associated inferential machinery, of the ageing effects on
banknote images be made available. This project will adopt advanced
Bayesian modeling and inferential methodology in developing note ageing
process models. NCR Labs collections of machine readable banknotes will be
employed in formally assessing model adequacy as well as the ability to
generate sample ageing profiles of banknotes. The theory, analysis, and
methodology developed within this project will push the boundaries of
spatio-temporal statistical modelling and presents a superb opportunity in
making important advances in computational statistics in general.

6. Statistical Machine Learning methods for fMRI Analysis – Prof. M.Girolami
Functional magnetic resonance imaging (fMRI) is providing the means for
both early detection of a number of neurodegenerative diseases and to
study their origin and the mechanisms underlying them. Multivariate
statistical methods show great promise in the systematic study and
analysis of fMRI images and associated genetic data. There are still many
methodological statistical challenges to be addressed in this research and
the student will have the opportunity to work in a cross-disciplinary
group seeking to develop appropriate statistical models and associated
methods for this ongoing research.