Machine Learning and Optimisation

The Machine Learning and Optimisation Group conducts world-leading research into a wide range of techniques and applications of machine learning, optimisation, data mining, probabilistic modelling, pattern recognition and machine perception. We span the field from new theoretical developments to large applications, and is currently supported by a number of research bodies, including EPSRC, BBSRC, and several industry partners.

The group has a wide range of consultancy experience, across numerous domains.

If you would like to request consulting services in any area of the group expertise, please contact us.

Postgraduate Research Projects

Machine Learning and Optimisation projects

Ke Chen projects

Nick Filer projects

John Keane projects

Ross King projects

Tingting Mu projects

Richard Neville projects

Jon Shapiro projects

Xiaojun Zeng projects


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REUNITE: Harnessing computer intelligence to revolutionise disaster response

In crisis, such as natural disasters, communication channels are destroyed and families and friends are separated. Many flee to relief camps, where there may be more than a million people. In camps of this size, it’s almost impossible to find loved ones. So, aid organisations provide services to help people find those they’ve lost.

Typically the process of obtaining and recording information is time-consuming, with aid workers interviewing those who are looking for loved ones and asking for their personal details, the details of those they have lost and how they were separated, all recorded onto paper. By manually searching these paper records, aid workers try to reunite people as fast as possible. However, there may be few aid workers with the language skills required to conduct interviews and paper records are difficult to search and may contain errors.

Working with Tearfund, a team from the Machine Learning and Optimisation Group developed the REUNITE system, which provides an alternative to this time-consuming process. REUNITE allows aid workers to record information from missing people’s relatives on Smartphones rather than paper and upload it to a central server, where it can be quickly transcribed, validated and analysed by trusted individuals. Colleagues away from the scene can then collate the data with further details gathered using social network-style techniques and pattern matching technology can then be used to match up individuals so that aid workers can be notified of matches between people in their care.

The benefits:
  • With REUNITE, reuniting families becomes quicker, easier and more secure.
  • Its 'crowd-sourcing' techniques make missing person details available securely to an unlimited number of users online, considerably increasing the available workforce.
  • Machine learning techniques identify and champion the most capable users, and improve the reliability of tasks carried out by 'the crowd'.

Staff Profile

Professor Ross King

Prof Ross King

Prof Ross King is a Professor of Machine Intelligence in the School of Computer Science and at the Manchester Institute of Biotechnology. He is best known for the development of Robot Scientists 'Adam' and 'Eve'; laboratory automation systems that use artificial intelligence (AI) techniques to automatically execute cycles of scientific experimentation. Adam was the first machine ever to discover novel scientific knowledge independently of its human creators. Eve is designed to automate early-stage drug development and is focussed on neglected tropical diseases, and has discovered lead compounds against malaria and African sleeping sickness.

Research interests

The automation of science, the application and development of machine learning/data mining to bioinformatic and chemoinformatic problems.

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Join us and become part of a revolutionary approach to PhD training. A UK PhD augmented by practical experience in creativity and innovation, scientific evaluation, and leading edge research in industry and academia. 

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