Data for Discovery

Program Vision

Program Leader: Associate Professor James McGree

Program Co-Leader: Professor Moe Thandar Wynn

The vision of this Core Research Program is to advance the medical, physical, technological and social sciences through the development and application of new statistical, analytical and computational methods in data acquisition, experimental design, data transformation and data analytics to promote rigorous scientific discovery and generate evidence-based insights.  This includes the development and application of:

  • Methods for conducting rigorous experiments in modern science
  • Methods for timely and cost-effective data acquisition
  • Data transformation methods to assess and remedy quality problems (including data provenance/traceability)
  • Data (and process) analytics (e.g., fair, accurate, transparent, privacy-preserving)

 Associate Investigators

Data Science @ QUT Representatives

Research Fellows



Potential student, Honours, Masters and PhD projects

  • New computational algorithms for adaptive clinical trials (Contact: Assoc Prof James McGree)
  • Experimental design methods for precision agriculture (Contact: Assoc Prof James McGree)
  • Preventing the spread of infectious diseases through designed experiments (Contact: Assoc Prof James McGree)
  • Understanding tumour growth through new experiments in biology (Contact: Assoc Prof James McGree)
  • New experimental design methods for efficiently monitoring environmental ecosystems such as the Great Barrier Reef (Contact: Assoc Prof James McGree)
  • Determining optimal flight paths for drones through designed experiments (Contact: Assoc Prof James McGree)
  • Real-time control of nonlinear dynamical systems with designed experiments (Contact: Assoc Prof James McGree)
  • Facilitating efficient inference of big data using designed experiments (Contact: Assoc Prof James McGree)
  • Optimisation of noisy, high dimensional and computationally expensive utility functions (Contact: Assoc Prof James McGree)
  • How to run globally distributed experiments: A case study in plant ecology (Contact: Assoc Prof James McGree)