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Science and Engineering a university for the real world

Statistics and Operations Research


We focus on statistical and computational methods for translating data into information and providing optimised solutions for complex decision-making processes.

Our world-leading researchers are devoted to:

  • teaching statistics and operations research with a strong, real-world focus
  • developing new methods for analysing data and making informed decisions
  • working closely with our partners to solve challenging, high impact problems in government and industry.
Associate Professor James McGree
Discipline Leader, Statistics and Operations Research

Our experts

Our discipline brings together a diverse team of experts who deliver world-class education and high profile research outcomes.

Explore our staff profiles to discover the amazing research our discipline is leading in statistics and operations research.

Meet our experts

Adjunct Professor Erhan Kozan
Adjunct Professor
Division / Faculty
Statistical Science,
School of Mathematical Sciences
Research field
Applied Mathematics
Adjunct Professor Helen MacGillivray
Adjunct Professor
Division / Faculty
Statistical Science,
School of Mathematical Sciences
Research fields
Other Mathematical Sciences

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We offer a major that will provide you with the methodology for analysing data using empirical, theoretical and computational tools.

Our students discover complex statistical techniques and concepts through applications and data sets from the real world, providing strong links between theory and application.

Bachelor of Mathematics (Statistics)

Bachelor of Mathematics (Operations Research)

Industry learning

Students in our capstone unit have worked with industry partner Seqwater to optimise contingency water carting operations in South East Queensland for off-grid communities during times of drought.

This unit provides the opportunity to conduct an operations modelling analysis to address an authentic real-world problem and provide actionable outcomes for our industry partners.

In this unit students learn about stakeholder engagement, work in a project team within a compliance framework, and learn the language of industrial projects.


Our research focus areas and their practical applications include:

Applied and computational statistics

Statistical and computational approaches for fitting sophisticated models to complex data sets found in real-world applications.

Applied Bayesian statistics

Bayesian networks and hierarchical models.

Bayesian statistics

Expertise in computational methods such as Markov chain Monte Carlo, sequential Monte Carlo and approximate Bayesian computation.

Linear and integer programming

Large and complex decision problems, such as:

  • supply chain optimisation
  • production planning
  • portfolio optimisation.

Large-scale data mining

Machine learning and computational approaches to analyse large data sets.


Computational techniques for intractable optimisation problems.

Optimal experimental design

Undertaking efficient scientific discovery through designed experiments.

Scheduling optimisation

Scheduling and resource allocation problems, including:

  • hospital theatre and bed planning
  • mine planning
  • transport and logistics.


Developing novel statistical tools for more accurate predictions

Project leader

Professor You-Gan Wang



Project summary

This project intends to develop novel statistical tools for more accurate prediction by taking account of model complexity and uncertainties associated with the fitting procedure. The project also plans to develop a novel shrinkage approach via new penalty functions to avoid over-fitting and asymptotic properties.

The key applications may include genetic studies where the number of predictors is large and biological experiments where multivariate and temporal data are often collected - for example economical breeding in animal and fish farming and more effectively detecting the genes of interest in genetic studies on human, animals and plants.

Computationally efficient Bayesian algorithms estimating complex models

Project leader

Dr Christopher Drovandi



Project summary

This project aims to develop an integrated mathematical approach to synchronise and optimise patient scheduling systems of different departments to ensure that the hospital's assets and related resources are used efficiently.

We aim to investigate patient flow, process delay, and the interaction and inter-dependence of departments within the hospital to reduce access block (bottleneck) and subsequent overcrowding. This project aims to smooth the running of the hospital, improve the efficiency of patient throughput, reduce waiting times, and revolutionise hospital planning and scheduling.

Interdisciplinary and inter-institution projects

Some of the projects we are contributing to with other disciplines and institutions are:

View our student topics

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