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Overview

We focus on developing and examining methods for identifying useful information from noisy data and providing optimised solutions for complex decision-making processes.

We achieve this through the construction and testing of operational and stochastic models of complex systems to help better understand dynamic relationships.

Teaching

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.

Research

Our research focus areas and their practical applications include:

Applied Bayesian statistics

Bayesian networks and hierarchical models.

Bayesian computational statistics

Markov chain Monte Carlo applications to complex model fitting.

Discrete-event simulation

Operational modelling and 'what if' analysis, for applications including:

  • rail yard operations
  • hospital patient flow
  • port operations.

Linear and integer programming

Large and complex decision problems, such as:

  • supply chain optimisation
  • production planning
  • portfolio optimisation.

Large-scale data mining

Machine learning techniques and computationally intensive algorithms, such as random forests.

Meta-heuristics

Computational techniques for intractable optimisation problems.

Scheduling optimisation

Scheduling and resource allocation problems, including:

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

Simulation-based model validation

Assessment of external accuracy through re-sampling and cross-validation techniques.

Projects

Developing novel statistical tools for more accurate predictions

Project leader

Professor You-Gan Wang

Dates

2016-2019

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

Dates

2016-2019

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.

An integrated mathematical approach to synchronise and optimise hospital operations

Project leader

Adjunct Professor Erhan Kozan

Dates

2014-2017

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.

Bayesian statistics model to address climate change effects on the Great Barrier Reef and better understanding of neurological diseases

Project leader

Professor Kerrie Mengersen

Dates

2014-2016

Project summary

Bayesian statistics is a fundamental statistical and machine learning approach for density estimation, data analysis and inference. However, there remain open questions regarding the formulation of the model, the likelihood and priors, and efficient computation.

This project proposes new approaches that address these issues, and applies them to two exemplar challenges: the impact of climate change on the Great Barrier Reef and better understanding neurological diseases related aging, in particular Parkinson's Disease.

Large-scale statistical machine learning

Project leader

Professor Peter Bartlett

Dates

2011-2016

Project summary

This research program aims to develop the science behind statistical decision problems as varied as web retrieval, genomic data analysis and financial portfolio optimisation. Advances will have a very significant practical impact in the many areas of science and technology that need to make sense of large, complex data streams.

Agri-intelligence in cotton production systems - stage one

Project leader
Dates

2017

Project summary

Agri-intelligence describes the seamless integration of deep agricultural knowledge across industry value chains, systems science and powerful digital technologies to help farming enterprises make best use of data to improve operations.

This project will take first major steps into the development of agri-intelligence for cotton production systems by developing an understanding of the decision space for agri-intelligence, as well as developing insight into the value of information from the cotton production value chain for decision making on farms.

National Cancer Atlas

Project leader

Distinguished Professor Kerrie Mengersen

Dates

2017

Project summary

Building on the successful Atlas of Cancer in Queensland project – the National Cancer Atlas will be an online, interactive tool that allows health agencies, policy makers and the community to understand the location and resource requirements for the most common cancers diagnosed in Australia, including breast, prostate and colorectal cancers.

Underpinned by complex statistical models, the resource will have a strong focus on using novel visualisation methods that guide end users to appropriately interpret the significance of geographical patterns.

This is a collaboration between QUT, CRCSI, CCQ and the AIHW, bringing together leading skills in statistical modelling, epidemiological research, digital architecture and a suite of data products. Find out more about this project at the CRCSI website.

Interdisciplinary and inter-institution projects

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

View our student topics

Our topics

Are you looking to study at a higher or more detailed level? We are currently looking for students to research topics at a variety of study levels, including PhD, Masters, Honours or the Vacation Research Experience Scheme (VRES).
View our student topics

Our experts

We host an expert team of researchers and teaching staff, including Head of School and discipline leaders. Our discipline brings together a diverse team of experts who deliver world-class education and achieve breakthroughs in research.

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