We specialise in data management, machine learning, statistics, data visualisation and programming environments, across scale simulation, optimisation and machine learning.
Economics and innovation analytics
Led by Associate Professor Alfons Palangkaraya, this project investigates how data-driven techniques and artificial intelligence can drive developments in finance and economics. In partnership with the financial technology (fintech) industry, government and start-ups, we focus on:
- complex analysis within and across national innovation systems (e.g. patents) and how they are linked to economic performance
- analysis of research impact (publications vs patents vs project outcomes vs products)
- complex large graph and network analysis using machine learning and pattern analysis.
Led by Professor Shonali Krishnaswamy, this project focuses on important issues facing the financial sector today with respect to digital disruption, including:
- digital identity and know your customer: development of multi-modal and continuous identity verification and authentication to provide a seamless and secure customer experience
- regulatory technology: developing machine learning and AI-based systems that can enable regulatory compliance with greater assurance, while at the same time increasing efficiency and reducing cost
- blockchain technology: building innovative distributed and secure platforms under-pinned by blockchain and cybersecurity to enable seamless trade, finance and capital markets transactions
- API access: looking at innovative strategies for innovation to flow into financial institutions through enabling API-based access to data and infrastructure through modular-services-based interfaces at scale.
Medical data analytics
Led by Associate Professor Patrick Then, this project is partnering with corporations, government and other research groups to gather life sciences data and analytics in the following areas:
- personalised healthcare data: shifting from a healthcare system geared towards reactive, hospital-based treatment of acute conditions to one that is more community-based with a preventative and anticipatory approach
- ethics, privacy and security: recognising that information security is a key factor in the effective management and sharing of information resources
- health data incubator: aiming to provide large-scale and secure computing facilities, with robust data management and curation services. Accessing tools and resources for computational modelling, data analytics and visualisation.