Currently studying for a PhD in translational immunology and machine learning, I would describe myself as a passionate data scientist with an interest in the application of machine learning to diagnosis and management of disease. In my research I am using existing technologies, as well as generating new methodologies, to mine immunological data sets with the objective to uncover patterns that are predictive of cause or outcome in infectious disease (with a particular focus on acute severe sepsis).
I have a rich background with 5 years experience in routine diagnostic microbiology, working for organisations such as the John Radcliffe Hospital in Oxford, and Public Health England in Bristol. Throughout my career, I have always addressed my work with a multi-disciplinary mindset, eager to apply my programming and technical skills to problems faced day-to-day in the NHS.
I am active in the open-source programming community and many of my projects can be found on GitHub. Notable contributions are: CytoPy, an open-source Python framework for the analysis of large and complex Cytometry data, and ProjectBevan, a research database for COVID-19 biomarker discovery.
Burton RJ, Ahmed R, Cuff SM, Artemiou A, Eberl M. 2020. CytoPy: an autonomous cytometry analysis framework. bioRxiv 10.1101/2020.04.08.031898. (In-press)
Raffray L, Burton RJ, Baker SE, Morgan MP, Eberl M., 2020. Zoledronate rescues immunosuppressed monocytes in sepsis patients. Immunology 159:88-95.
Burton RJ, Albur M, Eberl M, Cuff SM. 2019. Using artificial intelligence to reduce diagnostic workload without compromising detection of urinary tract infections. BMC Medical Informatics and Decision Making 19:171.