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Turing Institute research programmes will help build smarter cities

Data science for science programme will bring modern computational methods to areas which have historically been understood qualitatively

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Turing Institute believes AI applied to large data sets will become a dominant research methodology
Turing Institute believes AI applied to large data sets will become a dominant research methodology

The Alan Turing Institute has launched two research programmes in urban analytics and data science for science.

 

These will join its seven other programmes in areas such as artificial intelligence (AI), government engineering and defence.

 

Transformative insights

 

Led by Mark Birkin, Turing fellow and professor of spatial analysis and policy in the School of Geography, University of Leeds, the urban analytics programme will focus on the process, structure, interactions and evolution of agents, technology and infrastructure within and between cities across spatial and temporal scales.

 

“The Institute has announced its urban analytics programme at an opportune time. New data from ‘smart cities’ is providing transformative insights all over the world," said Professor Birkin. "Devices ranging from wearables to smart tickets will permit deeper understanding of behaviour and lifestyles, economic prosperity, mobility and health, with positive impacts for business, planners and policymakers as well as the scientific community.

 

"The Turing is ideally placed to exploit these opportunities through the advancement of methods ranging from visual simulation to artificial intelligence.”

 

Turing’s programme will support assessments, projections and interventions which determine the economic and social welfare of people, businesses, governments and third sector agencies.

 

In addition to government and public planning, it is relevant to organisations across sectors including retail, financial services, mobility, health, policing and utilities.

“I firmly believe that the use of artificial intelligence methods, applied to large data sets, will become a dominant research methodology across the whole range of science and humanities"

It is becoming easier and easier to collect large amounts of data across a broad range of research areas, and there is a growing need to understand how this can best be exploited to make new discoveries. The data science for science programme aims to ensure that the research community (in science, arts and humanities) can make effective use of state-of-the-art methods in artificial intelligence and data science.

 

The programme will facilitate collaborative working with researchers from all disciplines across the Turing’s university partner network and with national research facilities. It will be led by Jonathan Rowe, Turing fellow and professor of natural computation in the School of Computer Science at the University of Birmingham.

 

Interdisciplinary projects

 

“I firmly believe that the use of artificial intelligence methods, applied to large data sets, will become a dominant research methodology across the whole range of science and humanities," said Professor Rowe. "The Alan Turing Institute is in a unique position to help make this happen. I will be working with our partner universities, national labs and international institutions to develop truly interdisciplinary projects, bringing expertise in data science and AI to bear on major research questions.”

 

Adrian Smith, institute director and chief executive, Turing, said that data science and artificial intelligence are wide-ranging sciences which are set to have a considerable impact on many different areas of research: "This is clear through the rapid growth of the Institute within such a short time, as we expand to nine research programmes.

 

“In our urban analytics programme, data science and AI tools and expertise will be developed and deployed alongside spatial analysis, geostatistics and a wide variety of disciplinary perspectives. In our data science for science programme we will bring modern computational methods to areas which have historically been understood qualitatively.”

 

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