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Glen Colopy

Photo May 2016CDT in Healthcare Innovation DPhil Student

CDT in Healthcare Innovation
Institute of Biomedical Engineering
Department of Engineering Science
University of Oxford
Old Road Campus Research Building
Oxford, OX3 7DQ


    • MSc, University of Oxford, (2012)
    • MSc, North Carolina State University, (2011)
    • BSc, The College of William and Mary in Virginia. (2009)

    DPhil Supervisor

    •  Dr David Clifton

    Research Topic

      •  Machine Learning Methods for Identifying Physiological Deterioration in Acutely-Ill Patients  

    Abstract: The Biomedical Signal Processing cluster at the IBME has shown that integrating physiological data into a single decision support system can allow early warning of patient deterioration, and that clinicians acting on the outputs of such a system can improve patient outcomes.  Existing work has typically made strong assumptions about the nature of the physiological data (treating them in a population-generic manner).  Recent work has investigated the use of Bayesian non-parametric regression using multi-task Gaussian processes (GPs) for modelling physiological time-series in a patient-specific manner.  The goal of this project will be to treat these processes in a “strongly Bayesian” manner, with distributions over all hyperparameters of the GP – to date, either maximum a posteriori estimates of the hyperparameters, or simple approximations to the joint distribution of the hyperparameters, have been used.

    This project will link with the Machine Learning Research Group (MLRG) in the IEB, where Prof. Steve Roberts and Prof. Michael Osborne will be key collaborators.  Their work has shown that Bayesian quadrature can provide an efficient means of estimating the joint distribution over the hyperparameters of a GP.  Further extensions have shown that change-point detection may be performed straightforwardly using the GP regression framework in a coherent probabilistic manner.



    Selected Journal Publications

    Full List: Google Scholar, ResearcherID


    Conference Publications


    Awards / Prizes / Accomplishments



    •  The Clarendon Fund, (4 years)

    Public Engagement With Science


    Other (e.g. Entrepreneurship)