Dom Danks began his doctoral studies at The Alan Turing Institute and the University of Birmingham in September 2019. 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.
Dom’s research focuses on the development and application of health-relevant probabilistic machine learning methods. His research interests within machine learning are broad, but he largely focuses on generative modelling, survival analysis and constrained machine learning. During his PhD he has worked on numerous projects with varying degrees of methodological and clinical emphasis. Most recently he has been developing methods to understand complex multimorbidity and treatment response as part of the NIHR AIM-OPTIMAL project.