Invited Speakers
- John Haslett, Statistics, Trinity College Dublin.
- Sujit Sahu, Mathematics, Southampton.
- Manfred Opper, AI, Berlin.
- Chris Glasbey, Biomathematics & Statistics Scotland, Edinburgh.
- Geir Storvik, Mathematics, Oslo.
Overview
Recent years have witnessed a considerable amount of research on modelling spatio-temporal systems both in an engineering and scientific domain. The importance of the couplings between spatial and temporal dimensions means that there is an increasing awareness of the inadequacy of models which collapse either the spatial or temporal dimensions. Coupled with routine collection of spatiotemporal data, in applications ranging from the microscopic scales of systems biology to the global scales of oceanography, there exists a strong motivation to develop general modelling techniques to aid the analysis of spatiotemporal systems.
The combination of novel theoretical problems, important applications areas and mix of disciplines offers real potential for some intellectual excitement. The workshop aims to bring together the various communities (machine learning, control, statistics, geosciences, econometrics, ecology etc.), focusing on the theoretical aspects of spatiotemporal modelling and encouraging cross pollination of ideas. It is envisaged that the workshop will be of interest to researchers from statistics, computer science, engineering and physics.
- Submission Deadline (Extended): August 7th 2009
- Author Notification: August 31st 2009
- Workshop: 12th - 14th October 2009
The workshop will be a two-day , single-track workshop, with two half days and one full day in between. We have 6 invited speakers and would like to encourage submission of abstracts for additional talks and posters. The oral sessions will be relatively short, punctuated by breaks and poster sessions to encourage discussion. We hope that submissions will come from a range of topics and applications surrounding spatiotemporal modelling, for example:
- learning and inference
- multi-scale modelling
- experiment design
- spatiotemporal covariance functions
- heterogeneous models
- sensor networks
- disease mapping
- ...