Danielle Charlotte Belgrave is a British computer scientist who specialises in using statistics and machine learning to understand the progression of
diseases … very relevant at the moment!
She currently works at Microsoft Research. While at school in Trinidad her Maths teacher inspired her to become a data-scientist (a field that uses scientific methods, processes, algorithms and systems to extract knowledge and insights from various data types.)
After gaining a degree in statistics at University College London she went on to achieve a PhD in machine learning in health applications at The University of Manchester – being supported by the Microsoft research
scholarship. For this, she was awarded a Dorothy Hodgkin award as well as the Barry Kay award. She studied atopic march (the natural progression of allergic diseases that begin early in life) and then used a latent disease model (latent meaning not yet manifesting the usual symptoms) to study
atopic march in over 9,000 kids, using machine learning to identify groups of children with similar early eczema patterns.
Belgrave is interested in using big data for clinical interpretation, to create personalised prevention strategies. Machine learning is making fast advancements, all leading to more precise healthcare – including discovering disease subtypes and to development of personalised health care advice.
In 2015 she was awarded the GlaxoSmithKline Exceptional Science award for her statistical methodological work, while at the same time working in respiratory medicine in pharmaceuticals.
She is currently involved in project Talia – a project with an aim of exploring how a human-centric (putting the user’s desires/needs at the centre of development) approach to machine learning can meaningfully assist in the detection, diagnosis, monitoring and treatment of mental health issues – a problem that Danielle believes to be under-investigated part of machine learning.
This piece was written by Stemette Society member, Nell Anders.