Stochastic Modelling and Operations Research

Stochastic Modelling and Operations Research involves using mathematics to understand and make decisions in systems that involve randomness and/or uncertainty.

Based within the School of Mathematical Sciences, our Stochastic Modelling and Operations Research group fuses theory, computation and data to address exciting and important problems throughout the sciences and industry.

We have particular strengths in:

  • modelling stochastic systems, particularly using computational techniques
  • calibrating stochastic models
  • modelling networks and their processes, with applications in energy and the Internet
  • modelling in ecology, epidemiology and evolution
  • Bayesian computational statistics
  • data science.
Snapshot of conversations on social media prior to a major public event

This network shows a snapshot of conversations on social media prior to a major public event. Nodes represent individuals and links represent mentions on Twitter. Studying the network topology enables us to identify different classes of users (organisers versus  advertisers), and through investigating the timing and content of messages we can make inferences about information flow over the social network. Download the social media conversation network chart.

Research impact

The Stochastic Modelling and Operations Research group’s advances are of direct relevance and benefit wherever uncertainty and/or randomness exists. This includes, for example, industries and areas such as:

  • the Internet
  • cyber security
  • defence
  • power grids
  • biosecurity
  • conservation science
  • health care systems
  • infectious disease epidemiology.

The Stochastic Modelling and Operations Research group has received significant recognition for its valuable contributions to industry and society, including prestigious medals from the Australian Academy of Science and Australian Mathematical Society.

Some recent real-world-impact highlights include:

  • providing new epidemiological insight into influenza and seasonal epidemics through work with the Australian Sentinel Practices Research Network
  • developing algorithms to estimate pandemic strains’ transmissibility and severity
  • advising the Australian Government’s Office of Health Protection on pandemics, including the Australian Health Principal Protection Committee (AHPPC) on the COVID-19 pandemic
  • developing tools for predicting events using open social media data, which are now being built into commercial forecasting tools for government use.

Our researchers

We have expertise across a wide range of areas. Many of our researchers are available to assist with research project supervision for Master of Philosophy and Doctor of Philosophy students.

Research team Expertise
Dr Andrew Black Stochastic modelling in ecology; Epidemiology and evolution; Bayesian computational statistics; Computational probability
Associate Professor Lewis Mitchell Computational social science; Natural language processing; Ensemble data assimilation and prediction
Dr Giang Nguyen Matrix-analytic methods; Brownian motion; Modelling of electricity pricing and power grids
Professor Joshua Ross Stochastic modelling in ecology, epidemiology and evolution; Bayesian computational statistics; Computational probability
Professor Matthew Roughan Network modelling; Internet measurement and management; Traffic analysis
Dr Peter Ballard  
Dr Oscar Peralta Gutierrez  

Contact us

To enquire about consulting or working with us on a research project, please contact our lead researcher within the School of Mathematical Sciences:

Associate Professor Lewis Mitchell

Senior Lecturer, School of Mathematical Sciences.

visit researcher profile

Higher degrees by research

Whether you intend to work in research or industry, a higher degree by research can give you a competitive edge throughout your career. Find out more about studying a Master of Philosophy or Doctor of Philosophy.

Higher Degrees by Research