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Prof Daniel Alexander
Department of Computer Science, UCL
Gower Street
London
WC1E 6BT
Appointment
  • Professor of Imaging Science
  • Dept of Computer Science
  • Faculty of Engineering Science
Biography

I am Director of the Centre for Medical Image Computing (CMIC) and deputy head of the Computer Science Department at UCL. I lead the Microstructure Imaging Group and the Progression of Neurodegenerative Diseases initiative. I am theme lead for the UCLH Biomedical Research Centre Healthcare Engineering and Imaging theme. I coordinate the Horizon 2020 EuroPOND consortium. My core expertise is in computer science, computational modelling, machine learning, and imaging science. My first degree was a BA in Mathematics from Oxford completing in 1993. I then studied for MSc and PhD in Computer Science at UCL, completing in 1997. After a post-doc at the University of Pennsylvania, I returned to UCL as a lecturer in 2000 and I have been Professor of Imaging Science since 2009. More details here.

Research Summary

My background is in mathematics and computer science. My expertise is in computational modelling, pattern recognition, and machine learning for biomedical imaging and data analysis.  My research aims to add understanding and capability in biomedicine by drawing on ideas from medical imaging, computer vision, data science, and machine learning. Much of my work focusses on imaging and the fusion of information from imaging with other data types (imaging + X).


The Microstructure Imaging Group works towards non-invasive histology. The idea is to use non-invasive imaging techniques, such as MRI to estimate features of tissue microstructure, such as cell size, shape and packing density, that traditionally require invasive biopsy and microscopy. Key applications are in brain connectivity mapping, white matter diseases like multiple sclerosis, and tumour-grading and treatment-planning for cancer. 


The POND group aims to learn the progression pattern of diseases - the appearance and development of symptoms and pathologies - from patient or population databases. Much of the work has focussed on neurodegenerative diseases, such as Alzheimer's disease and other dementias, using image data bases.


My work on image quality transfer (IQT) aims to estimate high quality from low quality images using machine learning. For example, we might estimate from a patient image acquired on a standard hospital scanner, the image we would obtain from the same patient in a high powered bespoke experimental scanner. The ideas help enable next-generation low-power portable imaging devices and enhance imaging capabilities in lower-and-middle income countries.


Teaching Summary

I teach the advanced MSc course Computational Modelling for Biomedical Imaging. Previously, I have designed and delivered lecture courses on computer programming, data structures, image processing, computer vision, and research methods.  I was director for the advanced MSc in Vision Imaging and Virtual Environments (VIVE), now renamed MSc Computer Graphics Vision and Imaging (CGVI), in the computer science department from 2005-2008. 


Academic Background
1997 PhD Doctor of Philosophy – Computer Science University College London
1994 MSc Master of Science – Computer Science University College London
1993 BA Bachelor of Arts – Mathematics University of Oxford
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