Data intensive research groups in WA generating world class research

The Robotics and Automation discipline at UWA addresses challenges in engineering where continuous human presence is either undesirable or impossible, and where teleoperation and automation can improve operations.

Digital technology is transforming our lives, professionally and personally. Agriculture has been a latecomer to this change but is catching up fast through significant investment into new technologies such as sensors, Internet of Things (IoT), drones, robotics, analytics and much more.

Using innovative techniques, the Complex Data Modelling research group at UWA develops mathematical, statistical and computational methodology to support engineering projects.

Computation now fundamentally underpins the majority of internationally competitive research across all fields and disciplines. The Curtin Institute for Computation (CIC) was established to meet this increasing demand for computational modelling, data analytics, and visualisation.

ICRAR’s Data Intensive Astronomy program’s main focus is the development of the data management and processing technologies required for the SKA.

Everyone benefits from work that is meaningful and productive. In a time of constant transition, the Future of Work Institute seeks to understand and improve work opportunities. They partner with groups and organisations from all sectors to implement, evaluate, and support change.

The Health Research and Data Analytics Hub has been established to extend Curtin’s ability to utilise large-scale health datasets from a variety of administrative and clinical settings to better understand care practices associated with positive health outcomes and help train a future workforce in health data analytics.

The Intelligent Virtual Environment and Simulation (IVES) aims to identify and nurture promising research areas in the disciplines of intelligent systems and interactive virtual environment. IVES focuses on intelligent data analytics, data science, data mining, pattern recognition, 3D virtual environments (including Virtual and Augmented Realities), simulation, human computer interactions and interfaces, learning analytics, and computational intelligence techniques

The Machine Learning Applications for Physical Sciences (MAPS) research cluster focus on the application of state-of-the-art Machine Learning algorithms for efficient processing, accurate characterisation and robust prediction of signals arising in physical sciences.

The quantum information, simulation and algorithms discipline researches the potential to harness nature at a deeper level while developing new possibilities for communication and data processing.

This group’s research develops optimisation, scheduling and control solutions for the mining and offshore extraction sectors and associated operations, such as transport, energy supply and the servicing of remote communities.