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4 Year MRC PhD Programme: The Artificial Co-Investigator: AI Systems as Team Members in Discovery for Medicine.

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  • Full or part time
    Prof C A Reed
    Dr K Budzynska
    Dr Philip Quinlan
  • Application Deadline
    No more applications being accepted
  • Competition Funded PhD Project (European/UK Students Only)
    Competition Funded PhD Project (European/UK Students Only)

About This PhD Project

Project Description

We are becoming increasingly familiar with the idea of AI as a team member in demanding cognitive environments (see this article [http://www.bbc.co.uk/news/technology-41010848] commissioned from us by BBC News, for example). In previous work we have shown how AI reasoning systems can synthesise medical research hypotheses that go on to be testable and publishable (Quinlan et al., 2012). Our goal in this project addresses the MRC’s Discovery for Medicine agenda, and TTL in particular (MRC Delivery Plan 2016-2020), through an ambitious unification of these two threads of work, using recent results in deep learning to build AI systems that contribute to collaborative research teams in natural language – and we’ll know we’ve succeeded when the AI system is listed as a co-author on published results.

The era of precision medicine is promising to deliver individualised patient treatment, but, whilst individual genetic traits can be identified, the ability to direct specific treatment is lacking. The UK is delivering some of the largest datasets worldwide that can be analysed, such as the release of data by UK Biobank. These datasets offer the opportunity for AI to find credible combinations of genetic mutations that may be candidates for drug development.

In a collaboration between the University of Dundee and the Advanced Data Analysis Centre (ADAC) at the University of Nottingham, the student will (a) develop deep learning systems for identifying regularities in genetic datasets from the UK Biobank; (b) build on existing infrastructure to develop new software for human-machine hybrid research teams; and (c) explore innovative ways of explaining and justifying research hypotheses.

Track Record: Chris Reed is Professor of Computer Science and Philosophy at the University of Dundee in Scotland, where he heads the Centre for Argument Technology. Chris has been working at the overlap between argumentation theory and artificial intelligence for over twenty years, has won £6m in funding and has almost 200 peer-reviewed papers in the area. He collaborates with a wide range of partners such as IBM and the BBC, and is also active in public engagement and commercialisation of research, having served as executive director (CTO, CSO and CEO) of three start-up companies, and appearing in TV, radio and print media with a combined audience in excess of 29 million people.

Pease, A., Lawrence, J., Budzynska, K., Corneli, J. & Reed, C. (2017) "Lakatos-style collaborative mathematics through dialectical, structured and abstract argumentation", Artificial Intelligence, 246, pp181-219.

Quinlan, P., Thompson, A. & Reed, C. (2012) "An analysis and hypothesis generation platform for heterogeneous cancer databases" in Proceedings of the 4th International Conference on Computational Models of Argument (COMMA 2012), IOS Press, Vienna.

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