This partnership represents an unrivalled opportunity to transform many domestic and international industries. The combined skill set of the international collaboration between the two institutions will result in outstanding outcomes that neither could achieve alone.
A Memorandum of Understanding (MoU) has been signed by the University of Sydney’s multidisciplinary Centre for Translational Data Science and The Alan Turing Institute, the national institute for data science and artificial intelligence (AI) of the UK.
According to a recent report, the MoU is for the collaboration on joint research projects that are of strategic importance to the Australian economy.
These will include criminology, air quality, and geosciences. The collaboration will be centred on The Institute’s Data-centric Engineering Programme.
This is a great partnership both for the Centre and the University. To have reciprocal skills exchange with an institute of such renown is an advantage for the University’s growing pool of research and partnerships.
The Centre was established in 2016. It is one of only a few centres worldwide that has a research focus of modelling complex phenomena.
The translation of this research then turns into practical outcomes, which benefit society.
Three areas of collaboration in data science already underway are space-time models for environmental air quality monitoring and improvement, probabilistic modelling for Australia’s natural resources, and Bayesian optimisation for criminology.
The first collaboration focuses on an air quality and improvement project.
The Data-Centric Engineering Programme is already working with the London Mayor’s Office on a joint project to understand and improve air quality over London.
This is done by developing advanced spatio-temporal statistical and machine learning methods for estimating and forecasting air pollution levels at a hyper-local scale.
These are further linked to critical monitoring stations and policy interventions.
The Centre is working with the Nature Conservation Fund to develop statistical machine learning models to assess the impact that ‘greening’ cities has on air quality, and how this improvement in air quality affects health outcomes, vital for sustainable cities of the future.
This partnership represents an unrivalled opportunity to transform many domestic and international industries.
The combined skill set of the international collaboration between the two institutions will result in outstanding outcomes that neither could achieve alone.
The second project has the two centres working with statisticians, machine learners and earth scientists from the Universities of Sydney and Western Australia.
They will use the latest advances in data science to transform the process by which decisions are made in the management of natural resources.
Discussions are underway with IAG, McKinsey, Rio Tinto, Lloyds Registry, and government agencies to form a multidisciplinary partnership that will drive transformational advances in the earth sciences.
This will build scale and human capacity in the resources and environment industries.
The collaboration will allow data science researchers in Australia to tap into the wealth of experience that exists within the Turing Institute to revolutionise the way decisions are made in this important industry sector.
The third project will focus on criminology.
Statistical models can help understand criminal behaviour and determine the key drivers and dynamics of crime.
Their collaboration is developing new Bayesian optimisation algorithms to uncover hidden patterns in criminal activity.
Modelling the dependence between different types of crime leads to greater understanding of the dynamics criminal behaviour.
The Institute is looking forward to continuing this research with colleagues at the Centre.
Australia’s first international symposium on ethical algorithms and data science will be hosted by the University on 12 – 14 December 2018.
The symposium will bring together experts from diverse disciplines such as ethics, law, and AI to exchange views on the viability, legitimacy, ethics and complexity of algorithmic decision-making.
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