CIC Specialties

Big data refers to data sets that are so large or complex that traditional data analysis techniques are not suitable and so require new ways of processing the data. The big data analytics performed by the CIC are crucial for next-generation radio telescopes such as the Murchison Widefield Array and the Australian component of the Square Kilometre Array.

The CIC uses the available data to develop the best methods and models in machine learning and artificial intelligence to achieve the most accurate predictions. The CIC has experience in machine learning across a number of fields, such as computer vision, Natural Language Processing, and time series forecasting. The CIC can develop prediction models using both supervised – for classification and regression –and unsupervised learning methods – such as clustering and density estimation.

High-performance computing (HPC) uses supercomputers and clusters to provide solutions to complicated problems. HPC involves parallelising, containerising, and automating to wrangle massive amounts of operations that involve large amounts of data. The CIC frequently works with partners either to deploy solutions on the Pawsey and NCI supercomputers or to operationalise data pipelines using commercial solutions such as AWS and Google Cloud.

Research software development is an emerging field where software engineers support scientific research through code optimisation and development. Data engineering relates to the building of those automated processes and systems required to collect and analyse large, complex datasets. For example, the CIC developed and optimised code to modernise existing workflows to better use HPC resources, making said workflows more applicable to a larger range of data.

The CIC’s dedicated Project Management Office supports and manages projects end-to-end. The CIC does early scoping to determine project activities, resourcing requirements (time, skills, experience) and overall scope – including deliverables and costings. Projects are tracked and managed with appropriate processes and by implementing purpose-designed tools.

The CIC develops data and statistical models by using theoretical, semi-empirical, and empirical approaches. The CIC can work with domain experts to develop physics-based models as well as to model inversion methods using stochastic or deterministic optimisation.

With the aid of a computer, simulation recreates phenomena based on current knowledge. The CIC generates such simulations and the subsequently extracted datasets – often vast and complex – help researchers from disparate fields, such as the space industry, medicine and mining.

The CIC aims to build computational skills across the university community by integrating relevant units into undergraduate curricula in all faculties. The CIC will also supervise the data science projects of interns at the Hub for Immersive Visualisation and eResearch; and Innovation Central Perth.