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Data science research collaboration MOU signed

12 October 2018
Can data science help to solve real-world problems?
The Centre for Translational Data Science and The Alan Turing Institute have signed a memorandum of understanding to collaborate on joint research projects of strategic importance to the Australian economy.

The University of Sydney’s multidisciplinary Centre for Translational Data Science and The Alan Turing Institute, the UK’s national institute for data science and artificial intelligence, have signed a memorandum of understanding to collaborate on joint research projects. These will include criminology, air quality, and geosciences that will have strategic importance to the Australian economy. The collaboration will be centred around The Alan Turing 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 a boon for our growing pool of research and partnerships.
Professor Sally Cripps, Co-Director of the Centre for Translational Data Science

The Centre for Translational Data Science was established in 2016 and is one of only a few centres worldwide that has a research focus of modelling complex phenomena and the translation of this research 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 optimization for criminology. All of these areas of collaboration will make a real difference in the world," said Professor Sally CrippsCo-Director of the Centre for Translational Data Science.

Professor Mark Girolami from The Alan Turing Institute and Professor Sally Cripps from the Centre for Translational Data Science sign the memorandum of understanding

Air quality and improvement

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 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 for Translational Data Science 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.
Professor Mark Girolami, The Alan Turing Institute

Geosciences and natural resources

The second project has the two centres working with statisticians, machine learners and earth scientists from the Universities of Sydney and Western Australia to 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 to drive transformational advances in the earth sciences to build scale and human capacity in the resources and environment industries.

“The world’s economic, societal and environmental future depends upon the balance we place on competing outcomes when making decisions and policy surrounding the use of our natural resources.  Despite the importance of this issue, many decisions and policies are based on limited amounts of data and rudimentary data analysis, with little appreciation of the critical role that understanding and quantifying uncertainty plays in the process. 

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,” said Professor Cripps.

“This collaboration opens up huge potential for exciting new projects. We look forward to exchanging researchers with the University of Sydney to strengthen the existing Memorandum of Understanding and jointly-funded collaborations we have,” says Professor Mark Girolami, Programme Director for Data-Centric Engineering, The Alan Turing Institute

“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,” concluded Professor Girolami.

Criminology

The Centre for Translational Data Science criminology research is led by Dr Roman Marchant, who works closely with Turing Fellows, Associate Professor Theo Damoulas and PhD student Louis Ellam.

“Statistical models can help us to understand criminal behaviour and determine the key drivers and dynamics of crime. In particular, to achieve a future reduction in crime arising from data-driven and informed policy decisions,” says Dr Merchant.

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 Turing is looking forward to continuing this research with colleagues at the Centre for Translational Data Science,” said Associate Professor Damoulas.


The University of Sydney will host Australia’s first international symposium on ethical algorithms and data science from 12–14 December, 2018. The symposium will bring together experts from diverse disciplines such as ethics, law, and artificial intelligence to exchange views on the viability, legitimacy, ethics and complexity of algorithmic decision-making.  

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