Matt G Hall

ORCID iD
orcid.org/0000-0002-9530-5477
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Matt Hall

Sources:
Matt G Hall (2014-06-03)

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United Kingdom

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Matt G Hall (2016-01-24)

  • Keywords
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Diffusion MRI,

Sources:
UCL ORCID Registration (2014-06-03)

Monte-Carlo simulation

Sources:
UCL ORCID Registration (2014-06-03)

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UCL IRIS Profile

Sources:
UCL ORCID Registration (2014-06-03)

Biography

I'm an itinerant computational physicist who has found his way into MRI research, and diffusion MRI in partiular. My PhD is from Imperial College London where I worked on statistical mechanical models of biological evolution with Prof Henrik Jensen. I worked closely with the Tangled Nature model, which I helped formulate and carried out a lot of the ground-work investigation of the model's behaviour. Next stop was a small consultancy in the City of London called Eurobios, where I worked on bespoke logistics optimisation software for the Danish Post-Office. This was a lot of Java and Simulated Annealing. A little later I found myself as St George's, University of London where I got my first introduction to diffusion MRI. Here I worked on a comparison of fibre-crossing reconstruction techniques using HARDI data. I then moved to Danny Alexander's group at UCL where I designed and built the Camino Monte-Carlo simulation and applied it to diffusion MRI data synthesis. This has underpinned the development a range of diffusion MRI reconstruction techniques. I also ran a nice line in anomalous diffusion and fractional diffusion imaging techniques. At this point I went mildly insane and went off to seek fortune and adventure in finance. I missed, and ended up working for the regulator. I was an FSA supervisor in the London Markets for about 2 years, where I gained a new-found appreciation for Kafka, and spent some time designing analysis tools for the regulation of insurance firms and a collaboration with the Bank of England. Having got that out of my system, I then returned to diffusion MRI research where I have continued to develop advanced simulation methods and anomalous diffusion-based approaches using GPU computing.
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