The motivation for the research programme itself derived from the realization that, with the spectacular evolution of computing facilities and the proliferation of applications in which the number of experimental units is comparatively small but the underlying dimension is massive, it has become necessary to fit complex models for which the effective number of parameters is very large. Areas of application include image analysis, microarray analysis, finance, document classification, astronomy and atmospheric science. Methodological advances have been made, but with them comes the need for further development and appropriate theoretical underpinning. The workshop will review the state-of-the-art of this rapidly developing field. Timed as it is at the beginning of the six-months research programme, the workshop will be broad in scope, with the aim of laying the groundwork for research interactions during the rest of the programme and beyond. Particular topics likely to be covered include the following: strategies for explicit and implicit dimension-reduction; classification methods for complex datasets, including machine-learning approaches; asymptotic theory for increasing dimension; graphical and other visualisation methods for complex datasets; and presentation of topical case studies, probably drawn from the areas of application listed above.
The workshop will therefore cover fundamental areas of modern statistical theory and methodology required for the analysis of important large-scale practical problems. The nature of the topics is such that it should be of interest to those working in machine learning research and others in computer science, as well as to mainstream statisticians.