Conformal Predictors in Machine Learning

Applications are invited for a PhD studentship in the field of Machine Learning at Royal Holloway, University of London. This is a fully-funded EPSRC iCASE award with support from Thales UK as the industry partner.

We are an elite Computer Science department with world-class experts in Machine Learning and several other fields. Research in Machine Learning and Pattern Recognition research is developed in the Computer Learning Research Centre (CLRC) - details are available at http://clrc.rhul.ac.uk. The students enjoy a very lively research culture and are fully involved in various activities of the Department.

The project will involve research into a novel machine learning technique in developing conformal predictors (CPs) when additional or privileged information is available. It is known that privileged information techniques developed by V.Vapnik make it possible to achieve better accuracy when applying support vector machines (SVM+). The PhD study will consider problems of anomaly detection, cluster analysis and other problems in the framework of conformal predictors when some privileged information can substantially improve the quality and reliability of prediction. The student will investigate theoretical underpinning of the method with experimental verification using real or synthetic data from appropriate scenarios and sources. A study also includes a generalisation of conformal predictors, beyond i.i.d. (independent and identically distributed) assumption, referred to as the online compression model (OCM), and how the privileged information would influence the results of predictions.

The essential requirements for a suitable applicant are: a first or upper second class honours degree in Computer Science or a related discipline; excellent mathematical and analytical skills; the ability to engage fully with the industrial partner; and meet English language requirements. Among desirable skills are some experience in data analysis and a background in the Physical Sciences.

Starting date: as soon as possible but before 31 March 2016.

Interested candidates may contact Professor Gammerman informally before submitting their application. Applications must be made on-line to Royal Holloway, University of London https://www.royalholloway.ac.uk/studyhere/researchdegrees/applying/home.aspx and must include a cover letter, curriculum vitae and the names and contact details of three referees.