Research in statistics is wide ranging, both in terms of areas of applications and in terms of focus, from questions very closely related to a particular type of data (e.g normalization of microarrays) to theoretical questions at the intersection of mathematical statistics and modern probability theory.
Research in statistical learning theory is concerned with finding a predictive function based on data drawing upon different fields of statistics, functional analysis, optimization and computer science. Computational statistics aims to design algorithms for implementing statistical methods on computers, including the ones unthinkable before the computer age (e.g., bootstrap, wavelets, multiscale image processing), as well as the ability to cope with analytically intractable problems. It includes many computationally-intensive statistical methods.
Specific research areas: Model selection and model building, variation Bayes, inverse problems, statistical networks, Markov chain Monte Carlo methods, expectation-maximization methods, density estimation and generalized additive models.
Our research group utilises quantitative reasoning including mathematical modelling, statistical analysis to study complex data and in parallel building algorithmic tools for the analysis of complex systems and high dimensional data. In particular, the areas of bioinformatics examine such datasets that generated through high throughput modern biotechnological assays such as next generation sequencing technologies. Analysing of these data enable us to gain insight into fundamental biological processes.
Specific research areas: Cancer informatics, analysis of omics data, vertical integration, resting-state fMRI data, study of gene regulation and DNA relocation initiation.
This research area focuses on dynamic models of random processes and phenomena. Our research group utilises these method to solve applied problems in various fields such as finance, insurance, biology and medical science. In particular: stochastic volatility models for financial applications, biological modelling, finite and infinite non-negative matrices and their ergodicity and fractional processes.
Specific research areas: Trend diagnostics, model estimation, characterisations of probability distributions, mixture models, lose reserve models, geometric processes, Markov chains and Markov decision processes.
Econometrics refers to the application of mathematics, statistical methods, and computer science, to economic data and aims to give empirical content to economic relations. Time series analysis comprises methods for analysing time series data in order to extract meaningful statistics and other characteristics of the data.
Specific research areas: Nonlinear co-integrating regression, non-stationary time series econometrics, econometric theory, stochastic volatility models, long memory models, value-at-risk and expected shortfall.
Statistical theory provides a fundamental basis of all area in statistics. Asymptotic methods are used in all areas of statistics to provide approximations and are the basis of much of classical probability. In particular, the statistical analysis of extreme values is important for many disciplines, including finance, insurance and environmental sciences. Multivariate extreme value theory investigates among others the analysis of spatial extremes, the estimation of support curves and risk assessment of financial assets
Specific research areas: Extreme value theory, limit theorems for martingales, self-large deviations, saddle-point approximations, nonparametric estimation, change-point analysis and mixture models.
For information about opportunities to work or collaborate with us, please contact us.
Dr Lamiae Azizi
Graphical modelling and Variational Techniques, Bayesian non-parametrics, Bayesian modelling, clustering and classification; and spatial and spatio-temporal modeling. Applications of interest include image analysis, complex diseases (e.g. Cancers), FMRI and Genomics data.
Associate Professor Jennifer Chan
Generalised Linear Mixed Models, Bayesian Robustness, Heavy Tail Distributions, Scale Mixture Distributions, Geometric Process for Time Series Data, Applications for Insurance Data.
Dr. Ray Kawai
Statistics and numerical methods for stochastic differential equations, Mathematical ecology, mathematical finance, optimization.
Associate Professor Uri Keich
Bioinformatics: creating tools for the discovery and analysis of sequence motifs, study of DNA replication origins. Computational statistics: designing fast and numerically stable algorithms for evaluating the significance of exact tests.
Associate Professor Samuel Müller
Extreme Value Theory, Model Selection, Robust Methods, Applied Statistics.
Dr. John Ormerod
Variational Approximations, Generalised Linear Mixed Models, Splines, Data Mining, Semiparametric Regression and Missing Data.
Applied Statistics, Statistical Bioinformatics, Applications of Statistics in Medical Sciences, Computational Biology.
Associate Professor Shelton Peiris
Time Series Analysis, Estimating Functions and Applications, Statistics in Finance, Financial Econometrics, Time Dependent Categorical Data.
Dr. Michael Stewart
Mixture Models, Extremes of Stochastic Processes, Empirical Process Approximations, Density Estimation, Feature Selection, Applied and Computational Statistics
Dr. Emi Tanaka
Applied Statistics in Agriculture and Bioinformatics, Linear Mixed Models, Experimental Design, Computational Statistics
Robust Statistics, Data Visualisation, Model Selection, Econometric Modelling, Educational Research, Meat Science and Biostatistics.
Associate Professor Qiying Wang
Nonstationary time series econometrics, Nonparametric statistics, Econometric Theory, Local Time Theory, Self-normalized limit theory.
Dr. Rachel Wang
Statistical Network Modelling, Statistical Machine Learning, Computational Biology.
Dr Diana Warren
Development of Statistical Literacy, Probability Distributions, History of Mathematics and Statistics
Professor Jean Yang
Applied Statistics, Statistical Bioinformatics, Statistical machine learning, Integrative Analysis of Omics Data, Statistical networks and fMRI data.
Dr Pengyi Yang
Signalling Network Reconstruction, Transcription Network Reconstruction, Statistical Learning in Omics, Omic Data Visualisation, Decipher Embryogenesis
Visiting Professor Nicholas Fisher
Statistics for directional data, Nonparametric statistics, Graphical methods, Statistics in the Earth Sciences
Emeritus Professor John Robinson
Resampling Methods, Asymptotic Methods in Statistics Modelling and Inference in Biology.
Emeritus Professor Eugene Seneta
Finite and infinite non-negative Matrices and their Ergodicity, Probability Inequalities, History of Probability and Statistics.
Emeritus Professor Neville Weber
U-statistics, Exchangeability, Probability Limit, Generalized Linear Models, Asymptotic Approximations.Opportunities