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Statistics research

Research expertise

Computational statistics including bayesian statistics and modelling

Bayesian statistics offer a powerful paradigm for the development of scientific knowledge. Our research includes probabilistic modelling, stochastic computation, the use and implementation of Monte Carlo Markov Chains, and other approaches in Bayesian inference and decision theory.

We work on the development of computationally efficient statistical methods for complex models with applications in genetics, and MCMC algorithms with applications in epidemiology, geophysics and transportation.


Data analytics

The success of businesses, government and institutions is increasingly dependent on their ability to transform data into information, insights and novel data-products. We have expertise in data analysis in a number of industry sectors to work with large datasets, both stored and incoming to discover knowledge in the mountains of data.


Data mining

We apply cutting-edge methods from computer science and statistics to model patterns and relationships, typically using large datasets. We research and apply techniques such as machine learning, image analysis, and pattern recognition to model data from a wide variety of fields including business analytics, social media analysis, genetics and ecology.


Multivariate analysis

The plethora of data available in large systems (such as genetics, ecology, bioinformatics and the health sciences) requires the analysis of multiple variables simultaneously. We research and develop rigorous statistical methods for the analysis and visualisation of high-dimensional systems, graphical models, ordination, and spatial statistics.


Statistics theory and methodology

We have strengths in the development of statistical methodology and accompanying theory for smoothing methods; spatial statistics; reliability and survival analysis; and network tomography.


Sector-specific expertise


We have expertise in developing and implementing statistical methods for applications in the biological, ecological and health sciences, with expertise in linear and logistic regression, medical statistics and nutrition.


Ecological statistics

We create statistical tools for ecological applications, including models of ecological systems, species’ abundances, biodiversity and community ecology. We engage in active field-based ecological research, and consulting for environmental monitoring and impact assessment.


Quality control in manufacturing

Our scientists are developing new methods to assess quality in manufacturing processes. We have particular expertise in applications in the dairy industry.


Statistical methods in transportation science

We have expertise in statistical modelling and inference for transportation systems. Particular emphases include the development and analysis of models to describe the day-to-day dynamics of traffic flows on road networks, and methods of inference for traffic models using low-dimensional data (network tomography).


Statistical seismology and volcanology

We work on statistical problems in geophysics. Our focus is on the spatio-temporal estimation of hazard, especially from volcanoes or earthquakes, and the assessment of the risk.



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PhD scholarships

Kaupapa Māori multi-hazard risk assessment

The key focus will be on achieving positive outcomes for landscape and community resilience to meet Māori aspirations locally, regionally and nationally. There is flexibility to adjust the project scope and methodology according to the successful candidate’s interests, experience and expertise. The successful candidate will work alongside researchers and scientists in modelling, biophysical science, economics, social sciences and other disciplines to improve the way we provide information through risk-hazard modelling as a basis for sustainable planning of natural hazards, risk management, and improving resilience.

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Multi-capital and wellbeing dynamic value chain analysis

The purpose of this PhD scholarship is to encourage and support doctoral research investigating dynamic assessment of multi-capital and wellbeing value chains under coincident and cascading natural hazards events. This includes assessment of value chains through time, across space, for different socio-economic agents including households, businesses, government and not-for-profit organisations.

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Multi-impact operability modelling

The project is concerned with modelling the effect of multiple impacts to infrastructure by the same or different natural hazards. Impact from a previously occurring hazard can make the infrastructure more vulnerable to subsequent hazards. The requirement is to develop a vulnerability model (incorporating type of infrastructure, multiple hazard types, the magnitude of the subsequent hazards, the damage state from the earlier hazards, and repairs).

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Awards and recognition

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