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Research_

Intelligent systems

Applying machine learning to daily life
This research theme develops novel applications of machine learning for intelligent systems including robotics, predictive maintenance and process control. We collaborate with energy, agriculture and mining industries.   

Projects and partners include:

  • Modelling subsurface geology with Origin Energy
  • A whole-business data strategy for Sydney Water
  • Machine learned defence to adversarial RF systems via system identification and strategy optimisation
  • Path planning in dynamic environments – South Korean Agency for Defence Development.

 

Machine learned defence to adversarial RF systems via system identification and strategy optimisation

Vision: To develop techniques that learn the behaviour of an adversarial system and make informed autonomous decisions to oppose it.

Work: Develop Bayesian methods for dynamic online system identification and apply reinforcement learning to identify optimal counter-strategies.

Research impact: Bayesian methods for system identification can be applied to various dynamical problems in robotics and to many other data related problem such as speech recognition.

Collaboration team:

  • Associate Professor Fabio Ramos (Project lead)
  • Gilad Francis
  • Philippe Morere
  • Harrison Nguyen

Research focus: