Centre for Mathematical Sciences Research Seminars
  • Lecture Theatre 2, Roland Levinsky Building

  • Room A102, Portland Square Building

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The Centre for Mathematical Sciences research seminars and events are listed below.

The four main seminar series are in applied mathematics, pure mathematics, statistics and theoretical physics. Visit the centre's webpages for the latest seminar updates and information.

25 April: Trust in Numbers

  • Speaker: Professor Sir David Spiegelhalter, President of the Royal Statistical Society

Join the Royal Statistical Society South West Local Group for this public lecture by Professor Sir David Spiegelhalter OBE FRS, President of the Royal Statistical Society.

Those who value quantitative and scientific evidence are faced with claims both of a reproducibility crisis in scientific publication, and of a post-truth society abounding in fake news and alternative facts.

Both issues are of vital importance to statisticians, and both are deeply concerned with trust in expertise. By considering the ‘pipelines’ through which scientific and political evidence is propagated, David will consider possible ways of improving both the trustworthiness of the statistical evidence being communicated, and the ability of audiences to assess the quality and reliability of what they are being told. There will also be cheap laughs at numerous examples of disastrous communication of statistics in the media.

This lecture is free and open to all - no booking is required. Tea, coffee and biscuits will be available afterwards.

Contact Dr Yinghui Wei (yinghui.wei@plymouth.ac.uk) for any queries.

16 May: Progress on the connection between spectral embedding and network models used by the probability, statistics and machine-learning communities

  • Speaker: Patrick Rubin-Delanchy, University of Bristol

In this talk, Patrick gives theoretical and methodological results, based on work spanning Johns Hopkins, the Heilbronn Institute for Mathematical Research, Imperial and Bristol, regarding the connection between various graph spectral methods and commonly used network models which are popular in the probability, statistics and machine-learning communities. An attractive feature of the results is that they lead to very simple take-home messages for network data analysis: a) when using spectral embedding, consider eigenvectors from both ends of the spectrum; b) when implementing spectral clustering, use Gaussian mixture models, not k-means; c) when interpreting spectral embedding, think of “mixtures of behaviour” rather than “distance”. Results are illustrated with cyber-security applications.

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