My Research: Much of what we do in science is inference, which means we have some experimental data, from which we infer something about the Universe. For example we may wish to determine how fast the
Universe is expanding, or what the properties of Dark Energy are. We can also ask broader questions such as whether the Big Bang model is the preferred theoretical framework, or whether Einstein's General Relativity is favoured over another gravity theory. These are the sorts of questions I and colleagues at the ICIC address, through statistical analysis of cosmological data such as obtained from gravitational lensing, galaxy or microwave background surveys. My specific interests centre on developing and applying the best methods for extracting information from data, such as: using the full
3D information from weak lensing surveys, and using size as well as shape information; transforming the data so that theory can be applied more effectively; compressing data in an optimised way so statistical analysis can be done very fast; looking for non-Gaussianity in the Cosmic Microwave Background and in the galaxy distribution. The scientific questions I am interested in include whether or not the acceleration of the Universe is due to Einstein's cosmological constant, Dark Energy, or alternative gravity models.
Roles: Apart from being Director of the ICIC, I coordinate the Weak Lensing Magnification work package in the Euclid Consortium, was in the non-gaussianity group of Planck, served on the Science and Technology Facilities Council Science Board, co-chaired the STFC DiRAC High Performance Computing Resource Allocation Committee, and am External Examiner for the University of Manchester Physics degree.
Publications: Click here (opens in a new window)
Impact: Massive data compression techniques allow very rapid analysis of large data sets, such as come from medical scanners. I am a founding director of Blackford Analysis, a spin-out company from the University of Edinburgh, which specialises in making radiology tasks more efficient.