Statistics and Data Analytics

Staff

Dr Andrej Aderhold : Research Associate

Supervisor: Dirk Husmeier

  • Publications
  • Dr Craig Alexander : Lecturer

     

  • Personal Website
  • Dr Linda Altieri : Environmental Research Associate

    Dr Craig Anderson : Lecturer

    Research students: Xueqing Yin, Riham Ismail, Kamol Sanittham, Michael Waltenberger

  • Personal Website
  • Dr Mitchum Bock : Lecturer

  • Publications
  • Dr Agnieszka Borowska : Research Assistant

    Supervisor: Dirk Husmeier

  • Prof Adrian Bowman : Professor of Statistics

    Research students: Yinuo Liu, George Vazanellis

  • Personal Website
  • Publications
  • Dr Daniela Castro-Camilo : Lecturer

  • Personal Website
  • Dr Charalampos Chanialidis : Lecturer

  • Personal Website
  • Publications
  • Dr Christina A Cobbold : Reader

    Population dynamics of ecological systems; spatial ecology; evolutionary ecology in changing environments

    Member of other research groups: Mathematical Biology
    Research students: Renato Andrade, Parag Gupta

  • Personal Website
  • Publications
  • Dr Nema Dean : Lecturer

    Supervised and unsupervised learning; mixture models; variable selection; educational testing data; dynamic treatment regime estimation

    Research students: Shuhrah Alghamdi, Riham Ismail, Sebastian Martinez Bustos, Aldawarsi Bashayr, Alastair Gemmell

  • Personal Website
  • Publications
  • Dr Amira Elayouty : Lecturer

  • Ludger Evers : Lecturer (part-time)

    Research students: Benjamin Szili, Ivona Voroneckaja, Shuhrah Alghamdi, Dimitra Eleftheriou

  • Publications
  • Prof James Campbell Gemmell : Honorary Professor

    Prof Gemmell is chief executive of the Environment Protection Agency of South Australia.

  • Personal Website
  • Dr Mayetri Gupta : Reader

    Research students: Flynn Gewirtz-O'Reilly, Lanxin Li, Kannat Na Bangchang

  • Personal Website
  • Publications
  • Prof Dirk Husmeier : Chair of Statistics

    Machine learning and Bayesian statistics applied to systems biology and bioinformatics; Bayesian networks; statistical phylogenetics

    Research staff: Andrej Aderhold, Agnieszka Borowska, Benn Macdonald
    Research students: Shaykah Aldossari, Aldawarsi Bashayr, Dalton David, Alan Lazarus, Luisa Paun, Campioni Nazareno, Ionut Paun, Yalei Yang

  • Personal Website
  • Publications
  • Prof Janine Illian : Chair/Professor in Statistical Science

    Research student: Andrew Seaton

  • Dr Eilidh Jack : Lecturer

  • Dr Dimitra Kosta : LKAS Fellowship

    Markov chains, contingency table analysis, phylogenetics, maximum likelihood estimation, singular statistical models, Bayesian model selection.

    Member of other research groups: Geometry and Topology, Algebra

  • Personal Website
  • Publications
  • Prof Duncan Lee : Professor

    Spatiotemporal modelling; Bayesian methods; environmental epidemiology and disease mapping

    Research students: George Gerogiannis, Kamol Sanittham, Michael Waltenberger, Yoana Napier, Xueqing Yin
    Postgraduate opportunities: Mapping disease risk in space and time, Estimating the effects of air pollution on human health

  • Personal Website
  • Publications
  • Dr Marnie Low : Lecturer

  • Personal Website
  • Publications
  • Dr Vincent Macaulay : Reader

    Statistical genetics; population genetics; Bayesian methods; phylogenetics; GPs

    Research student: Suzy Whoriskey

  • Personal Website
  • Publications
  • Dr Benn Macdonald : Research Assistant

    Member of other research groups: Mathematical Biology
    Research student: Hanadi Alzahrani
    Supervisor: Dirk Husmeier

  • Dr Colette Mair : Lecturer

  • Prof Claire Miller (née Ferguson): Professor

    Environmental and ecological modelling; nonparametric smoothing; time series analysis; functional data analysis

    Research students: Jafet Belmont Osuna, Michael Currie

  • Personal Website
  • Publications
  • Dr Gary Napier : Lecturer

    Research student: Michael Waltenberger
    Postgraduate opportunities: Bayesian approaches to compositional data with structural zeros

  • Publications
  • Dr Tereza Neocleous : Lecturer

    Forensic statistics; quantile regression; semiparametric models; biostatistics applications

    Research student: Dimitra Eleftheriou
    Postgraduate opportunities: Bayesian approaches to compositional data with structural zeros

  • Personal Website
  • Publications
  • Dr Mu Niu : Lecturer

  • Dr Agostino Nobile : Honorary Research Fellow

    Bayesian statistics; MCMC and other Monte Carlo methods; mixture models; discrete choice models

  • Personal Website
  • Publications
  • Dr Ruth O'Donnell : Lecturer

  • Publications
  • Dr Theo Papamarkou : Lecturer

    Research students: Laura Stewart, Benjamin Szili, Dimitra Eleftheriou

  • Dr Surajit Ray : Senior lecturer

    Functional Data Analysis; Analysis of mixture models; high-dimensional data; medical image analysis; analysis of earth systems data; immunoinformatics

    Research students: Maryam Al Alawi , Salihah Alghamdi, Yangsong Cheng, Alastair Gemmell, Bader Lafi Q Alruwaili, Flynn Gewirtz-O'Reilly
    Postgraduate opportunities: Statistical Analyis of Medical images: Application to tumour detetection from PET imaging, Modality of mixtures of distributions

  • Personal Website
  • Publications
  • Prof Marian Scott OBE: Professor of Environmental Statistics

    Radio-carbon and cosmogenic dating-design and analysis of proficiency trials; environmental radioactivity; sensitivity and uncertainty analysis applied to complex environmental models; spatial and spatiotemporal modeling of water quality; flood risk modeling; environmental indicators; developing the evidence base for environmental policy and regulation

    Research students: Jafet Belmont Osuna, Michael Currie, Yoana Napier

  • Personal Website
  • Publications
  • Qingying Shu : Postdoctoral Research Fellow

    Supervisor: Xiaoyu Luo

  • Dr Ron Smith : Honorary Senior Research Fellow

  • Personal Website
  • Publications
  • Dr Ben Swallow : Lecturer

    Bayesian statistical inference; Markov chain Monte Carlo (MCMC) methods; data integration; model selection; stochastic processes

    Member of other research groups: Mathematical Biology

  • Personal Website
  • Prof Michael Titterington : Honorary Senior Research Fellow

    Statistical analysis of mixture distributions; latent structure analysis; pattern recognition; machine learning; smoothing and nonparametric statistics; optimum design of experiments

  • Personal Website
  • Publications
  • Dr Bernard Torsney : Honorary Research Fellow

    Non-parametric inference; optimisation; optimal experimental design; sampling theory; applications in economics; multiple comparisons

  • Personal Website
  • Publications
  • Dr Liberty Vittert : Mitchell Lecturer

  • Personal Website
  • Dr Vlad Vyshemirsky : Lecturer

    Research student: Lida Mavrogonatou

  • Publications

  • Postgraduates

    Maryam Al Alawi : PhD Student

    Supervisor: Surajit Ray

  • Salihah Alghamdi : PhD Student

    Research Topic: Analysis of Spatially correlated functional data objects.
    Supervisor: Surajit Ray

  • Jafet Belmont Osuna : PhD Student

    Supervisors: Marian Scott OBE, Claire Miller (née Ferguson)

  • Yangsong Cheng : PhD Student

    Research Topic: Computing, Inference and Applications of Hierarchical Mode Association Clustering
    Supervisor: Surajit Ray

  • Michael Currie : PhD Student

    Supervisors: Marian Scott OBE, Claire Miller (née Ferguson)

  • Dimitra Eleftheriou : PhD Student

    Supervisors: Tereza Neocleous, Ludger Evers, Theo Papamarkou

  • Flynn Gewirtz-O'Reilly : PhD Student

    Supervisors: Mayetri Gupta, Surajit Ray

  • Bader Lafi Q Alruwaili : PhD Student

    Research Topic: Clustering and Cluster Inference of complex data structures
    Supervisor: Surajit Ray

  • Yinuo Liu : PhD Student

    Supervisor: Adrian Bowman

  • Lida Mavrogonatou : PhD Student

    Supervisor: Vlad Vyshemirsky

  • Kannat Na Bangchang : PhD Student

    Supervisors: Mayetri Gupta, Manuele Leonelli

  • Yoana Napier : PhD Student

    Supervisors: Marian Scott OBE, Duncan Lee

  • Luisa Paun : PhD Student

    Supervisor: Dirk Husmeier

  • Kamol Sanittham : PhD Student

    Supervisors: Duncan Lee, Craig Anderson

  • Andrew Seaton : PhD Student

    Research Topic: Spatial Point Process Models
    Supervisor: Janine Illian

  • Laura Stewart : PhD Student

    Supervisors: Theo Papamarkou, Alexey Lindo

  • Benjamin Szili : PhD Student

    Supervisors: Ludger Evers, Theo Papamarkou

  • George Vazanellis : PhD Student

    Research Topic: Spatiotemporal models for environmental data
    Supervisor: Adrian Bowman

  • Ivona Voroneckaja : PhD Student

    Supervisor: Ludger Evers

  • Michael Waltenberger : PhD Student

    Supervisors: Duncan Lee, Craig Anderson, Gary Napier

  • Suzy Whoriskey : PhD Student

    Supervisor: Vincent Macaulay

  • Yalei Yang : PhD Student

    Supervisors: Hao Gao, Dirk Husmeier

  • Xueqing Yin : PhD Student

    Research Topic: Mapping disease risk in space and time
    Supervisors: Craig Anderson, Duncan Lee


  • Postgraduate opportunities

    Estimating the effects of air pollution on human health (PhD)

    Supervisors: Duncan Lee
    Relevant research groups: Statistics and Data Analytics

    The health impact of exposure to air pollution is thought to reduce average life expectancy by six months, with an estimated equivalent health cost of 19 billion each year (from DEFRA). These effects have been estimated using statistical models, which quantify the impact on human health of exposure in both the short and the long term. However, the estimation of such effects is challenging, because individual level measures of health and pollution exposure are not available. Therefore, the majority of studies are conducted at the population level, and the resulting inference can only be made about the effects of pollution on overall population health. However, the data used in such studies are spatially misaligned, as the health data relate to extended areas such as cities or electoral wards, while the pollution concentrations are measured at individual locations. Furthermore, pollution monitors are typically located where concentrations are thought to be highest, known as preferential sampling, which is likely to result in overly high measurements being recorded. This project aims to develop statistical methodology to address these problems, and thus provide a less biased estimate of the effects of pollution on health than are currently produced.

     

    Bayesian approaches to compositional data with structural zeros (PhD)

    Supervisors: Gary Napier, Tereza Neocleous
    Relevant research groups: Statistics and Data Analytics

    Compositional data are vectors comprised of non-negative parts of some whole and commonly occur in scientific disciplines such as chemistry, geology, economics, and many others, such as the budget of time on fitness applications. Zero measurements occur frequently in compositional data and cause many problems with the application of standard transformations used when dealing with compositions. There are two types of compositional zeros: rounded zeros, indicating the presence of a component but below some detection limit; and essential, or structural, zeros, denoting the absolute absence of a component from an observation, such as a fitness activity that a person may never participate in during their regime.

    One effective approach to modelling compositional data with an abundance of structural zeros, which may have several levels of variation, would be to firstly partition the data into subsets characterised by the same pattern of presence/absence of specific components; and then do (i) hierarchical/composite models or (ii) regression problems / variable selection on the compositions in each subset.

    This project aims to develop new methodology that incorporates the ad hoc subsetting of the data into the modelling process in a more seemless manner.

     

    Mapping disease risk in space and time (PhD)

    Supervisors: Duncan Lee
    Relevant research groups: Statistics and Data Analytics

    Disease risk varies over space and time, due to similar variation in environmental exposures such as air pollution and risk inducing behaviours such as smoking.  Modelling the spatio-temporal pattern in disease risk is known as disease mapping, and the aims are to: quantify the spatial pattern in disease risk to determine the extent of health inequalities,  determine whether there has been any increase or reduction in the risk over time, identify the locations of clusters of areas at elevated risk, and quantify the impact of exposures, such as air pollution, on disease risk. I am working on all these related problems at present, and I have PhD projects in all these areas.

     

    Modality of mixtures of distributions (PhD)

    Supervisors: Surajit Ray
    Relevant research groups: Statistics and Data Analytics

    Finite mixtures provide a flexible and powerful tool for fitting univariate and multivariate distributions that cannot be captured by standard statistical distributions. In particular, multivariate mixtures have been widely used to perform modeling and cluster analysis of high-dimensional data in a wide range of applications. Modes of mixture densities have been used with great success for organizing mixture components into homogenous groups. But the results are limited to normal mixtures. Beyond the clustering application existing research in this area has provided fundamental results regarding the upper bound of the number of modes, but they too are limited to normal mixtures. In this project, we wish to explore the modality of non-normal distributions and their application to real life problems

     

     

    Statistical Analyis of Medical images: Application to tumour detetection from PET imaging (PhD)

    Supervisors: Surajit Ray
    Relevant research groups: Statistics and Data Analytics

    Positron-emission tomography (PET) is a nuclear medicine functional imaging technique that is used to observe metabolic processes in the body and is often used for tumour detection. Unlike CT or MRI scans PET scans are more reliable as the target the metabolic process but are very expensive. There are only 5 PET scanners in the whole of Scotland and around 30 in England. Further, very limited information from the images is used by the radiologists to hand segment the tumour. It is often challenging to extract the tumour alone from the background of healthy tissues and image noise. In this project, we will explore existing methods for automatic segmentation of tumor based on PET images and develop a technique to implement automatic segmentation on anonymized PET images obtained at Gartnavel Hospital.