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Bio
Sam Greenbury is a Senior Research Data Scientist in the Research Engineering Group with a background in physics, computational biology, machine learning, and healthcare.
Following a PhD at the University of Cambridge in the TCM Group investigating fundamental properties of biological systems, he developed and led on analysis at the Department of Health for national policy priorities. He subsequently spent time at Imperial College London as a postdoc in both mathematics (Centre for Mathematics of Precision Healthcare) and biomedical data science (ITMAT Data Science Group) groups where he developed and applied Bayesian inference, machine learning and data science approaches to healthcare data.
At the Turing, he has worked on projects in health (Learning machines), trustworthy digital identity (Trustchain), and urban analytics (Urban Analytics Technology Platform).
He is passionate about writing code (particularly in Rust) to model/simulate systems/data.
Projects
Clim Recal
Collection of methods to de-bias climate projection data (sub-component of DyME-CHH but also used as independent codebase)
Popgetter
Get all the census you need or want using popgetter
Synthetic Population Catalyst
Tool that creates a synthetic population for any area within Great Britain, including socio-demographic, health and daily activity data. Calibrated to 2020, with projections of some variables to between 2012 and 2039.