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Research groups

Our collaborative research impacts the world
We work across industries to find solutions.

We work with:

  • philosophers and ethicists on the impact that artificial intelligence has on the human condition.
  • geoscientists to understand the possible evolutionary trajectories of Earth
  • medical doctors to design clinical trials which speed up the process of medical discovery while improving patient outcomes
  • vets to automate the dairy industry

Data fusion

Developing techniques to fuel meaningful research

Data fusion aims to develop the mathematical, statistical and algorithmic structures needed to build models of complex phenomena. Integral to this process is ensuring those models are predictive, testable and data centric.

Building predictive and testable data-centric models of complex phenomena in the physical, life and social sciences relies on data fusion – that is, developing fundamental mathematical, statistical and algorithmic infrastructure. Crucial to the process of discovery is the ability to make meaningful inference from observations that are varied in type, volume and precision. The theme will focus on the development of novel probabilistic models and methods to estimate them. These models provide a principled way to fuse information from multiple sources; information from observations, and information from domain specific theories, expert opinion and prior studies.

  • Reproducible research
  • Bayesian non-parametrics
  • Causal models
  • Bayesian networks
  • Deep learning
  • Statistical modelling
  • Dimension reduction
  • Missingness
  • Machine learned defence to adversarial RF systems via system identification and strategy optimisation
  • Origin Energy – modelling subsurface geology
  • Probabilistic datacubes for integral field spectroscopy
  • Localising geological formation boundaries via Bayesian joint inversion
  • Detrending radio transient light curves from the Murchison wide-field array
  • Markov chain Monte Carlo on function spaces
  • Bayesian risk prediction instruments
  • Inferring explosion mechanisms for type Ia supernovae
  • Space-time and demographics crime prediction
  • Academic capability mapping
  • Bias removal in MRI datasets
  • Bayesian optimisation for GLM applied to police patrolling
  • Modelling of crime using features from the environment
  • Modelling of crime using deep learning
  • Visualisation of spatial temporal density functions
  • A quantitative analysis for understanding the relationship between electronic gaming machines, problem gambling, socio-demographic variables and crime
  • Bayesian deep learning for uncertain environments
  • Irreproducibility; nothing is more predictable
  • Coevolutionary multi-task learning for feature-based modular pattern classification
  • Bayesian neural learning via Langevin dynamics for chaotic time series prediction
  • Spectral estimation for non-stationary multivariate timeseries
  • Applying machine learning to criminology: semi-parametric spatial-demographic Bayesian regression
  • A comparison of two models of dental care for Aboriginal communities in New South Wales
  • Bayesian geometric models for nutrition
  • Bayesian hierarchical model for the causes and consequences of a reduction in gestation length

The science of decision-making

How to make the right decision under pressure

Having to make important decisions causes headaches for many people. Our team studies the science of decision-making, creating research that will enable everyone from doctors to governments make better decisions.

We study the science of decision-making under uncertainty. Given probabilistic predictions of future events, how do we choose a sequence of decisions to maximise a particular objective? For example, given a dataset with diseases and outcomes of treatments, what treatments should be selected to improve the patient’s recovery? Our research is currently focused on multi-arm bandit settings, Bayesian optimisation and reinforcement learning. 

  • Partially observable Markov decision process
  • Reinforcement learning
  • Bayesian optimisation
  • Experimental design
  • Planning
  • Machine learned defence to adversarial RF systems via system identification and strategy optimisation
  • Bayesian risk prediction instruments
  • Space-time and demographics crime prediction
  • Academic capability mapping
  • Bayesian optimisation for GLM applied to police patrolling
  • Path planning in dynamic environments – South Korean Agency for Defense Development
  • Galactose metabolomics
  • A whole-business data strategy for Sydney Water
  • Dynamic adipocyte metabolomics
  • Transient candidate vetting using convolutional neural nets