We have witnessed massive advances in predictive modeling within the past decade, with machine learning models achieving superhuman performance in a variety of tasks. However, the notion that such models are a 'black-box' with little to no explanatory power has limited their impact on fields where erroneous data-driven decisions can have severe consequences, such as to do with our health. Health data in particular has the potential for transformational impact to our lives with the improved efficiency machine learning models have brought to other domains. There is a need for new computational approaches that can derive insight from health data while addressing these concerns.

In this dissertation, we describe novel computational methods on health data that do not just achieve high performance on singular performance metrics but chiefly to characterise the data. The methods combine insights from statistical and machine learning, network science, and Bayesian uncertainty quantification to improve our understanding of human health data through the lens of two main sources of data: the human brain as imaged by diffusion MRI, and human physiology as measured by wearable sensors. (1) We show how to find brain regions that are more cohesive within a population of interest, discovering that nearly 4% of white matter is associated with genetic similarity. (2) We quantify how informative an individual's attributes are for generative regions of white matter, and (3) also the dual problem of measuring how predictive a region of white matter is of an attribute. (4) Lastly, we demonstrate how to measure a cardiovascular transfer function from digital activity traces by learning from 'natural experiments' during daily living conditions, and show that these transfer functions are predictive of variables associated with cardiovascular health.