Dom Danks is a first-year Doctoral student at The Alan Turing Institute and the University of Birmingham. His multidisciplinary supervision team consists of Christopher Yau, Alastair Denniston, Andrew Beggs and Theodore Kypraios. His background lies in the fields of physics and machine learning, having studied for an MSci in Theoretical Physics at the University of Birmingham and an MSc in Computational Statistics and Machine Learning at UCL. He is particularly excited by the prospect of improving healthcare outcomes using modern statistical techniques. His doctoral research therefore focuses on developing statistical and machine learning methodologies which have applications within biomedical research.

Research interests

Stochastic disease modelling aims to apply probabilistic methods to disease biomarker data in order to learn features of a disease’s pathology. During his PhD, Dom aims to develop such methods and apply them to aid understanding of complex diseases. The first stage of his PhD will see him focus on ophthalmic imaging data in a collaboration with the newly created INSIGHT Health Data Research Hub for Eye Health. He will also investigate genomic analysis and the challenges posed by multiple data type integration and structure discovery. By studying multiple areas of biomedical science, Dom will identify common challenges across application domains that require novel methodological solutions and develop strategies to automate routine statistical processes.