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 student: Renato Andrade

  • 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, Robin Muegge, 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
    Postgraduate opportunities: Assessing risk of heart failure with cardiac modelling and statistical inference

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

    Research students: Andrew Seaton, Stephen Jun Villejo

  • Dr Eilidh Jack : Lecturer

    Research student: Robin Muegge

  • Prof Duncan Lee : Professor

    Spatiotemporal modelling; Bayesian methods; environmental epidemiology and disease mapping

    Research students: George Gerogiannis, Kamol Sanittham, Michael Waltenberger, Robin Muegge, Yoana Napier, Xueqing Yin

  • 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

  • Publications
  • Dr Tereza Neocleous : Lecturer

    Forensic statistics; quantile regression; semiparametric models; biostatistics applications

    Research student: Dimitra Eleftheriou

  • 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
    Research student: Stephen Jun Villejo

  • 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
  • Dr Xiaochen Yang : Lecturer

    Supervised learning; distance metric learning; hyperspectral image analysis


  • 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

  • Robin Muegge : PhD Student

    Research Topic: Estimating the effects of air pollution on human health
    Supervisors: Nema Dean, Duncan Lee, Eilidh Jack

  • 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

  • Stephen Jun Villejo : PhD Student

    Research Topic: A Bayesian Spatio-Temporal Model to Test for Stability of Risks for Spatially Misaligned Data
    Supervisors: Ben Swallow, Janine Illian

  • 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

    Predicting patterns of retinal haemorrhage (PhD)

    Supervisors: Peter Stewart
    Relevant research groups: Continuum Mechanics - Modelling and Analysis of Material Systems, Mathematical Biology, Statistics and Data Analytics, Continuum Mechanics - Fluid Dynamics and Magnetohydrodynamics

    Retinal haemorrhage (bleeding of the blood vessels in the retina) often accompanies traumatic brain injury and is one of the clinical indicators of `shaken baby syndrome'. This PhD project will give you the opportunity to develop a combination of mathematical and statistical models to help explain the onset of retinal haemorrhage. You will devise and implement image processing algorithms to quantify the pattern of bleeding in clinical images of haemorrhaged retinas. In addition, you will develop a mathematical model for pressure wave propagation through the retinal circulation in response to an acute rise in intracranial pressure, to predict the pattern of retinal bleeding and correlate to the images. Cutting-edge pattern recognition methods from Machine Learning and Bayesian modelling will be used to infer characteristic signatures of different types of brain trauma. These will be used to help clinicians in characterising the origin of traumatic brain injury and diagnosing its severity. This is an ideal project for a postgraduate student with an interest in applying mathematical modelling, image analysis and machine learning to predictive healthcare. The project will give you the opportunity to join a cross-disciplinary Research Hub that aims to push the boundaries of quantitative medicine and improve clinical decision making in cases of suspected non-accidental head injury using innovative mathematical and statistical modelling.

     

    Assessing risk of heart failure with cardiac modelling and statistical inference (PhD)

    Supervisors: Dirk Husmeier, Hao Gao, Xiaoyu Luo
    Relevant research groups: Mathematical Biology, Statistics and Data Analytics

    In recent years, we have witnessed impressive developments in the mathematical modelling of complex physiological systems. However, parameter estimation and uncertainty quantification still remain challenging. This PhD project will give you the opportunity to join an interdisciplinary research team to develop new methodologies for computational modelling and inference in cardio-mechanic models. Your ultimate objective will be to contribute to paving the path to a new generation of clinical decision support systems for cardiac disease risk assessment based on complex mathematical-physiological models. You will aim to  achieve patient-specific calibration of these models in real time, using magnetic resonance imaging data. Sound uncertainty quantification for informed risk assessment will be paramount. This is an ideal PhD project for a postgraduate student with a strong applied mathematics and statistics or Computer Science background who is interested in computational mechanics and adapting cutting-edge inference and pattern recognition methods from Machine Learning and Bayesian modelling to challenging cardio-mechanic modelling problems. The project will give you the opportunity to join a cross-disciplinary Research Hub that aims to push the boundaries of quantitative medicine and improve cardio-vascular healthcare by bringing cutting-edge mathematical and statistical modelling into the clinic.

     

    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.