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!
What is Data Science
She currently works at Microsoft Research. While at school in Trinidad her Maths teacher inspired her to become a data scientist. This is a field that uses scientific methods, processes, algorithms and systems to extract knowledge and insights from various data types.
Her Route Into STEM
She gained a degree in statistics at University College London. Then she went on to achieve a PhD in machine learning in health applications at The University of Manchester. This was supported by the Microsoft research scholarship. For this, she was awarded a Dorothy Hodgkin award as well as the Barry Kay award. During her PhD, she studied atopic march (the natural progression of allergic diseases that begin early in life). She 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 now interested in using big data for clinical interpretation, to create personalised prevention strategies. She is using machine learning because it is making fast advancements, all leading to more precise healthcare – including discovering disease subtypes and to development of personalised health care advice.
Awards & Current Research
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. This project has 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. This is a problem that Danielle believes to be an under-investigated part of machine learning.
This article was written by Stemette Society member, Nell Anders.
Last updated November 2020