Statistics and Data Analytics
Staff
Dr Andrej Aderhold : Research Associate
Supervisor: Dirk Husmeier
Dr Craig Alexander : Lecturer
Research student: Peter Radvanyi
Dr Linda Altieri : Environmental Research Associate
Dr Craig Anderson : Lecturer
Research students: Alison Smith, Xueqing Yin, Riham Ismail, Kamol Sanittham, Michael Waltenberger
Dr Jafet Belmont Osuna : Research Associate
Environmental statistics; species distributions modelling; spatial ecology; analysis of citizen science data; application of Bayesian methods to characterize biological communities in changing environments
Supervisors: Marian Scott OBE, Claire Miller (née Ferguson)
Dr Mitchum Bock : Lecturer
Dr Agnieszka Borowska : Research Assistant
Supervisor: Dirk Husmeier
Prof Adrian Bowman : Professor of Statistics
Research students: Yinuo Liu, George Vazanellis
Dr Daniela Castro-Camilo : Lecturer
Research students: Erin Bryce, Daniela Cuba, Chenglei Hu
Dr Charalampos Chanialidis : Lecturer
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
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(PGR), Aldawarsi Bashayr, Alastair Gemmell
Dr Amira Elayouty : Lecturer
Ludger Evers : Lecturer (part-time)
Research students: Benjamin Szili, Ivona Voroneckaja, Shuhrah Alghamdi, Dimitra Eleftheriou
Prof James Campbell Gemmell : Honorary Professor
Prof Gemmell is chief executive of the Environment Protection Agency of South Australia.
Dr Mayetri Gupta : Reader
Research students: Flynn Gewirtz-O'Reilly, Lanxin Li, Kannat Na Bangchang
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, Alan Lazarus, Benn Macdonald, Mihaela Paun
Research students: Shaykah Aldossari, Aldawarsi Bashayr, Dalton David, Campioni Nazareno, Ionut Paun, Yalei Yang
Prof Janine Illian : Chair/Professor in Statistical Science
My work focuses on spatial point process methodology with a focus on the development of modern, realistically complex, spatial statistical methodology that is both computationally feasible and relevant to end-users. During my career I have been enthudiastic about taing spatial point processes from the theoretical literature into the real world and is encouraging statistical development by fostering strong relationships with the user community. My work has has impcated on spatial modelling and biodiversity research in the context of ecological studies across many species, taxa and ecosystems. I also have a keen interest of applying realistically complex spatial models in other context, including crime modelling, earthquake forecasting, environmental modelling, epidemiology and terrorism studies.
Research staff: Andrew Seaton
Research students: Erin Bryce, Stephen Jun Villejo
Dr Eilidh Jack : Lecturer
Research student: Robin Muegge(PGR)
Prof Duncan Lee : Professor
Spatiotemporal modelling; Bayesian methods; environmental epidemiology and disease mapping
Research students: George Gerogiannis, Kamol Sanittham, Michael Waltenberger, Robin Muegge(PGR), Yoana Napier, Xueqing Yin
Postgraduate opportunities: Mapping disease risk in space and time, Estimating the effects of air pollution on human health, Forecasting Local Net-electricity Demand at Scale
Dr Marnie Low : Lecturer
Research student: Peter Radvanyi
Dr Vincent Macaulay : Reader
Statistical genetics; population genetics; Bayesian methods; phylogenetics; GPs
Research student: Laura Stewart
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 staff: Craig Wilkie, Jafet Belmont Osuna
Research students: Peter Radvanyi, Michael Currie
Dr Gary Napier : Lecturer
Research students: Catherine Holland, Michael Waltenberger
Dr Tereza Neocleous : Lecturer
Forensic statistics; quantile regression; semiparametric models; biostatistics applications
Research students: Dimitra Eleftheriou, Catherine Holland
Dr Mu Niu : Lecturer
Research student: Wenhui Zhang
Dr Agostino Nobile : Honorary Research Fellow
Bayesian statistics; MCMC and other Monte Carlo methods; mixture models; discrete choice models
Dr Ruth O'Donnell : Lecturer
Dr Theo Papamarkou : Lecturer
Research students: Benjamin Szili, Dimitra Eleftheriou
Dr Mihaela Paun : Research Associate
Supervisor: Dirk Husmeier
Dr Surajit Ray : Senior lecturer
COVID Resarch, Functional Data Analysis; Analysis of mixture models; high-dimensional data; medical image analysis; analysis of earth systems data; immunoinformatics
Research students: Salihah Alghamdi, Yangsong Cheng, Alastair Gemmell, Bader Lafi Q Alruwaili, Wenhui Zhang, Flynn Gewirtz-O'Reilly
Postgraduate opportunities: Modality of mixtures of distributions, Analysis of Spatially correlated functional data objects.
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 staff: Jafet Belmont Osuna
Research students: Michael Currie, Yoana Napier, Daniela Cuba
Dr Andrew Seaton : Research Associate
Supervisor: Janine Illian
Qingying Shu : Postdoctoral Research Fellow
Supervisor: Xiaoyu Luo
Dr Ron Smith : Honorary Senior Research Fellow
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 students: Stephen Jun Villejo, Chenglei Hu
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
Dr Bernard Torsney : Honorary Research Fellow
Non-parametric inference; optimisation; optimal experimental design; sampling theory; applications in economics; multiple comparisons
Dr Liberty Vittert : Mitchell Lecturer
Dr Vlad Vyshemirsky : Lecturer
Research student: Lida Mavrogonatou
Dr Craig Wilkie : Research Associate
Supervisor: Claire Miller (née Ferguson)
Dr Xiaochen Yang : Lecturer
Supervised learning; distance metric learning; hyperspectral image analysis
Dr Wei Zhang : Lecturer
Bayesian data analysis, Ecological statistics, Statistical computing
Member of other research groups: Continuum Mechanics - Modelling and Analysis of Material Systems
Postgraduates
Salihah Alghamdi : PhD Student
Research Topic: Analysis of Spatially correlated functional data objects.
Supervisor: Surajit Ray
Erin Bryce : PhD Student
Research Topic: Statistical landslide hazard modelling with a view towards
medium to long term territorial planning
Supervisors: Daniela Castro-Camilo, Janine Illian
Yangsong Cheng : PhD Student
Research Topic: Computing, Inference and Applications of Hierarchical Mode
Association Clustering
Supervisor: Surajit Ray
Daniela Cuba : PhD Student
Research Topic: Statistical tools to interpret soil variation
Supervisors: Daniela Castro-Camilo, Marian Scott OBE
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
Catherine Holland : PhD Student
Research Topic: Bayesian approaches to compositional data with structural zeros
Supervisors: Gary Napier, Tereza Neocleous
Chenglei Hu : PhD Student
Research Topic: Natural hazard risk estimation using Multivariate Extreme-Value
Mixture Models (MEVMM)
Supervisors: Daniela Castro-Camilo, Ben Swallow
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(PGR) : 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 : MSc Student
Supervisors: Marian Scott OBE, Duncan Lee
Peter Radvanyi : PhD Student
Research Topic: Groundwater monitoring design
Supervisors: Claire Miller (née Ferguson), Craig Alexander, Marnie Low
Kamol Sanittham : PhD Student
Supervisors: Duncan Lee, Craig Anderson
Alison Smith : PhD Student
Research Topic: Developing novel ways to represent spatial patterns in disease
risk
Supervisor: Craig Anderson
Laura Stewart : PhD Student
Research Topic: Development and application of stochastic models of
agglomeration
Supervisors: Vincent Macaulay, 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
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
Wenhui Zhang : PhD Student
Research Topic: Analysis of Positron Emission Tomography data for tumour
detection and delineation
Supervisors: Surajit Ray, Mu Niu
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.
Analysis of Spatially correlated functional data objects. (PhD)
Supervisors: Surajit Ray
Relevant research groups: Statistics and Data Analytics
Historically, functional data analysis techniques have widely been used to analyze traditional time series data, albeit from a different perspective. Of late, FDA techniques are increasingly being used in domains such as environmental science, where the data are spatio-temporal in nature and hence is it typical to consider such data as functional data where the functions are correlated in time or space. An example where modeling the dependencies is crucial is in analyzing remotely sensed data observed over a number of years across the surface of the earth, where each year forms a single functional data object. One might be interested in decomposing the overall variation across space and time and attribute it to covariates of interest. Another interesting class of data with dependence structure consists of weather data on several variables collected from balloons where the domain of the functions is a vertical strip in the atmosphere, and the data are spatially correlated. One of the challenges in such type of data is the problem of missingness, to address which one needs develop appropriate spatial smoothing techniques for spatially dependent functional data. There are also interesting design of experiment issues, as well as questions of data calibration to account for the variability in sensing instruments. Inspite of the research initiative in analyzing dependent functional data there are several unresolved problems, which the student will work on:
- robust statistical models for incorporating temporal and spatial dependencies in functional data
- developing reliable prediction and interpolation techniques for dependent functional data
- developing inferential framework for testing hypotheses related to simplified dependent structures
- analysing sparsely observed functional data by borrowing information from neighbours
- visualisation of data summaries associated with dependent functional data
- Clustering of functional data
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
Forecasting Local Net-electricity Demand at Scale (PhD)
Supervisors: Jethro Browell, Duncan Lee
Relevant research groups: Statistics and Data Analytics
Electricity supply and demand must balance in real-time, which is increasingly challenging as low-carbon technologies revolutionise energy production (wind, solar) and consumption (electric vehicles, heat pumps). Short-term forecasts are therefore essential to maintain an economic and reliable supply of electricity. Such forecasts are widely used in the energy sector, but forecasters face emerging challenges from new consumer behaviours, small scale generation and storage, as well as data quality, privacy, and security issues. This PhD project will give you the opportunity to develop statistical models to forecast electricity demand at regional and local levels of our continuously evolving energy system. Research themes include:
- Computationally efficient modelling and forecasting of 100s or 1000s of regions (or potentially millions of smart meters!).
- Adaptive modelling and forecasting in the presence of structural breaks.
- Probabilistic forecasting accounting for spatial and temporal dependencies and hierarchies.
The project provides an excellent opportunity to conduct cutting edge methodological development complemented by a practical application of societal importance. The successful candidate will need to be comfortable with interfacing with other disciplines and industry partners and be passionate about their research.