Fabian Sturman
CDT Student
Fabian Sturman is currently studying on the CDT course in Healthcare Data Science, having completed an integrated Masters' in Mathematics and Computer Sceince at the University of Oxford.
Fabian is interested in the efficient modelling of infectious disease for policy development, and the integration of modern statistical and Machine Learning techniques into model calibration and data analysis. Fabian undertook a Batchelor's dissertation supervised by Prof Jasmina Pavovska-Griffiths investigating the efficient calibration of HPVsim, an Agent Based Model (ABM) for HPV transmission and its impact in terms of cervical cancer. They have continued to work with Prof Pavovska-Griffiths, publishing this work as a methods paper in JTB and is currently working on further projects including efficient ABM calibration using Machine Learning surrogates, and modelling the potential effect of alternative cervical cancer screenign strategies if deployed by the NHS under unstable vacination uptake. Fabian is a member of the Mathematical Modelling for Public Health Policy Support group, at the Pandemic Sciences Institute, and thier representative to the Research Software Engineering (RSE) group.
Beyond healthcare modelling for pandemic sciences, Fabian has undetaken an 8 week reseach studentship at the Institute for Cancer Research in Sutton, London, investigating the use of image models to generate quantitative MRI from CT scans, and is currently undertaking a performing quantitative analysis for a project investigating novel metrics for weaning and exturbation for patients in Neurocritical Care Units. Fabian's Master's dissertation investigated the calculation of Probably Approximately Learning bounds for Reinforcement Learning agents in arbitary state- and action-spaces.