Optimal experimental design and inverse problems

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This two-day workshop, with attendees from academia and industry, aims to address the theory and practice in data acquisition design for reduced uncertainty in Bayesian inverse problems. Such problems are ubiquitous in earth, environmental, material and biomedical sciences. The workshop will involve short presentations and exploring specific challenges through discussion and group work.

The applications of geophysical exploration, structural integrity monitoring and material characterisation involve analytics for large sets of measurements acquired on a network of sensors. In the quest to make inferences about the surrounding media, these noisy data are subsequently processed by formulating and solving an appropriate inverse problem. This workshop aims to investigate how the statistical knowledge of a prior model can influence the data acquisition strategy, in order to maximise the expected information gain from a finite set of measurements. The workshop will involve short presentations and the exploration of specific challenges through discussion and group work.

Confirmed speakers:

Lior Horesh, IBM

Alen Alexanderian, North Carolina State University

This workshop is led by Nick Polydorides, Senior Lecturer at the University of Edinburgh and Faculty Fellow at The Alan Turing Institute.

If you are interested in attending or would like to find out more, please contact the Turing events team.

Foundation funded programme

The Alan Turing Institute-Lloyd’s Register Foundation £10 million programme into data-centric engineering.