Daniel C. Alexander

Professor Daniel C. Alexander BA MSc PhD is a computer scientist with expertise in pattern recognition, machine learning, computational modelling, medical imaging, and data analysis. He heads UCL’s Department of Computer Science, since 2024. He co-founded the UCL Centre for Medical Image Computing (CMIC) in 2005 and was Director 2015-2024. CMIC, now renamed the Hawkes Institute https://www.ucl.ac.uk/hawkes-institute, convenes scientists working at all stages of the imaging pipeline, from basic mathematics of image acquisition and reconstruction, through analysis and modelling, to front-line clinical-system deployment. The centre is a primary vehicle at UCL for translation of ideas from Engineering and Computer Science through to clinical research and practice. From an initial group of 30 researchers, Hawkes now includes over 25 PIs and more than 200 scientists spanning 10 UCL departments and 4 faculties. Daniel also leads the Healthcare Engineering and Imaging (HE&I) theme for the NIHR UCLH Biomedical Research Centre (BRC): a recurrent ~£100M per 5 year grant for translating basic research at UCL into clinical impact at UCLH and the NHS. He initiated and led the internationally renowned Microstructure Imaging Group (MIG: mig.cs.ucl.ac.uk) and Progression of Neurodegenerative Disease (POND: ucl-pond.github.io) initiative. MIG has made key innovations in microstructure imaging and non-invasive histology: the first demonstration of non-invasive maps of axon diameter, using ActiveAx (Alexander NIMG 2010); the first clinically viable microstructural MRI technique, called NODDI (Zhang NIMG 2012); and cancer-specific microstructure imaging with VERDICT MRI (Panagiotaki Cancer Research 2014). POND pioneered a new paradigm of data-driven models of temporal disease progression enabling uniquely fine-grained staging and stratification (Fonteijn NIMG 2012; Young Brain 2014). Both groups have a strong track record of clinical translation e.g. through the Camino toolkit www.camino.org.uk. Recent work focuses on machine learning for image reconstruction, e.g. using image quality transfer (Alexander NIMG 2017), and unsupervised learning for patient stratification and clinical-trial design from big healthcare data sets, e.g. (Young Nature Comms 2018, Eshaghi Nature Comms 2021, Vogel Nature Medicine 2021).