Faculty/School

Faculty of Science

School of Mathematical Sciences

Topic status

We're looking for students to study this topic.

Research centre

Centre for Data Science

Primary Supervisor

Dr Leah South
Position
Senior Lecturer in Mathematical Sciences
Division / Faculty
Faculty of Science

Overview

Monte Carlo methods use random sampling to approximate solutions to challenging problems. These methods are helpful for statistical models with many parameters, as discussed in this short video. The methods are particularly useful for Bayesian inference where one wishes to get a rigorous understanding of parameter uncertainty.

Despite having many advantages over their competitors, Monte Carlo methods can be very slow in the context of big data. In this project, you'll help develop scalable Monte Carlo methods to enable timely and reliable insights for big data.

The project is an opportunity to delve into cutting-edge research, gain practical experience in high-performance computing, and contribute to advancements in statistics.

Research engagement

The student will engage with:

  • Literature on scalable Monte Carlo methods
  • Computational statistics methods for estimating integrals and normalising constants
  • Statistical programming
  • Possibly statistical theory, depending on progress and interest

Research activities

Research activities include:

  • Regular meetings with Dr Leah South and other project supervisors/collaborators as relevant
  • Reading selected papers
  • Implementing methods in R (or Python/MATLAB)
  • Comparing methods on synthetic examples
  • Possibly theoretical investigation, depending on progress and interest

Research skills

The student will develop skills in:

  • Monte Carlo methods
  • Statistical programming
  • Simulation study design
  • Interpreting and communicating results
  • Potentially high-performance computing

Outcomes

  • Short literature review
  • New methods for estimating challenging integrals
  • Working code for selected methods
  • Numerical comparison of methods

Skills and experience

The following skills and experience are desirable:

  • Interest in statistics and programming
  • Some R, Python, or MATLAB experience
  • Interest in Monte Carlo methods or Bayesian computation
  • Willingness to learn new methods

Start date

2 November, 2026

End date

19 February, 2027

Location

QUT Gardens Point and online

Additional information

The successful student may apply for access to the High-Performance Computing system at QUT

Keywords

Contact

Dr Leah South

3138 8469

l1.south@qut.edu.au