Friday 14th March 2014 – Prof Chris Williams

Prof Chris Williams – University of Edinburgh

Switching Linear Dynamical Systems for Condition Monitoring in the Intensive Care Unit

Data drawn from an observed system is often usefully described by a number of hidden (or latent) factors. Given a sequence of observations, the task is to infer which latent factors are active at each time frame. In this talk I will describe the application of a switching linear dynamical model to monitoring the condition of a patient receiving intensive care. The state of health of a patient cannot be observed directly, but different underlying factors are associated with particular patterns of measurements, e.g. in the heart rate, blood pressure and temperature.

We demonstrate how to exploit knowledge of the structure of how the various latent factors interact so as to reduce the amount of training data needed for the system. A combination of domain knowledge engineering and learning is used to produce an effective solution. We use the model to infer the presence of two different types of factors: common, recognisable regimes (e.g. certain artifacts or common physiological phenomena), and novel patterns which are clinically significant but have unknown cause. Experimental results are given showing the potential of the system for the early detection of neonatal sepsis, a major clinical concern in the care of premature babies.

Chris Williams. Joint work with Yvonne Freer, Neil McIntosh, John Quinn, Ioan Stanculescu

Chris Williams is Professor of Machine Learning in the School of Informatics, and Director of the EPSRC Centre for Doctoral Training in Data Science in University of Edinburgh. He is interested in a wide range of theoretical and practical issues in machine learning, statistical pattern recognition, probabilistic graphical models and computer vision. This includes theoretical foundations, the development of new models and algorithms, and applications. His main areas of research are in visual object recognition and image understanding, models for understanding time-series, unsupervised learning, and Gaussian processes.

He obtained his MSc (1990) and PhD (1994) at the University of Toronto, under the supervision of Geoff Hinton. He was a member of the Neural Computing Research Group at Aston University from 1994 to 1998, and has been at the University of Edinburgh since 1998. He was program co-chair of NIPS in 2009, and is on the editorial boards of the Journal of Machine Learning Research, the International Journal of Computer Vision, and Proceedings of the Royal Society.

School of Computing Science & Digital Media, Robert Gordon University, Riverside East, Garthdee, Aberdeen, Conference Room N118, 14:10 – 15:10.


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School of Computing Science and Digital Media, Robert Gordon University, Aberdeen, Scotland
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