Innovative algorithms for analyses of large extragalactic data archives
Large archives of astronomical data (images, spectra and catalogues) are being assembled into a database that will soon be accessible worldwide as part of the Virtual Observatory. This necessitates the development of techniques which will allow fast, automated classification and extraction of key physical properties for very large datasets, and the ability to visualise the structure of highly multi-dimensional data, and extract and study substructures in a flexible way. In collaboration with various colleagues in the School of Computer Science at the University of Birmingham, we have been developing various algorithms and software involving genetic programming and evolutionary computation, latent variable analysis, computer vision, machine learning networks etc., in order to undertake various data mining activities using the virtual observatory. We have been recently granted a PPARC E-science postdoctoral fellowship for this activity. A PhD project in this area would suit students who are interested in innovative statistical techniques and programming, and provide further job opportunities in both astrophysics and computer science, as well as the data mining industry.
This project is part of the Astrophysics and Space Research Group: http://www.sr.bham.ac.uk/phd/index.php
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