Data Science & AI

Theme Lead: Professor Magnus Rattray

Data Science or Data Analytics is about the processes involved in extracting meaning from the abundance of data that are now available. It is a cross-disciplinary field that offers many opportunities for researchers across the university.

Institute for Data Science & Artificial Intelligence

Website: www.datascience.manchester.ac.uk

The University of Manchester's Institute for Data Science & Artificial Intelligence was created to act as an access point to the University’s expertise in data science and artificial intelligence, facilitates interactions between  researchers and problem holders, owns the University’s data science strategy, and will deliver sustainable support for the community.

Manchester has an engaged data science and artificial intelligence community of over 600 investigators, with methodologists embedded in Schools across the University addressing problems in extracting meaning from data, managing data volume, the variety of data used in analyses, the velocity with which it is produced and the veracity of those data.

Data science has a home in all three of the University's faculties (Science and Engineering; Humanities; Biology, Medicine & Health Sciences). Our expertise covers the complete data science life-cycle: from information management, through analytics, to practical applications. A key feature of our approach is very close coupling between methodologists and translational scientists, drawing on strength-in-depth in real-world applications of data science. This creates a virtuous circle, where challenging real-world problems drive the methodology research agenda, whilst providing a natural route to exploiting new algorithms and methods. We believe this deeply multidisciplinary approach is one of the distinctive features of data science at Manchester.

Scope of Theme

Data Science and AI are becoming increasingly successful in unlocking new knowledge and powering smart applications of digital technology. The theme brings together methodologists from across the University, sharing expertise organising, interpreting, discovering patterns in, and making predictions from complex data. A key feature of the University’s approach is very close coupling between methodologists and translational scientists, drawing on strength-in-depth in real-world applications of data science. The theme includes:

• Machine learning and statistics
• Information management
• Text analytics
• Image analytics
• Numerical algorithms
• Privacy and anonymisation
• Advanced processor technologies