I am an Associate Professor at the University of Oxford and lead Health Data Research UK’s national implementation project on reproducible machine learning. My research group at Oxford develops reproducible methods to analyse wearable sensor data in very large health studies to better understand the causes and consequences of disease. For example, we have developed methods to objectively measure physical activity in UK Biobank which are now actively used by researchers worldwide to demonstrate new associations with cardiovascular disease, depression, mood disorders, and others. We have also developed machine learning methods to identify sleep and functional physical activity behaviours such as walking. In addition, we have discovered the first genetic variants associated with machine-learned sensor phenotypes. This work shows the first genetic evidence that physical activity might causally lower blood pressure. In 2015 I was one of only three EU Marie Curie Award winners (from ~9000 fellowship holders), selected for my contributions to health sensor data analysis. I have also contributed to the creation of guidelines on the use of mobile devices in clinical trials, in collaboration with the US Food and Drug Administration (FDA) supported Clinical Trials Transformation Initiative on “Mobile Clinical Trials”.