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ARIA Opportunity Space: Extending Our Perception
AI for Physiological Supersensing
Advances in physiological sensing are enabling us to measure the brain and body with increasing resolution. Our ability to derive insights from this data is bottlenecked by human interpretation, both in the time taken to analyse the data and in the patterns we are able to identify. For example, the definition of deep (N3) sleep is still based on an arbitrary EEG threshold chosen in the 70s. These human-defined compressions of the data are unlikely to be the optimal predictors of the endpoints we ultimately care about, such as identifying the onset and progression of neurodegenerative diseases, or the risk of sudden cardiac death. We are using AI to address this challenge, by automating the analysis of physiological signals under existing paradigms, and by developing new ones.
Jonathan holds a DPhil in Machine Learning from Oxford, where he was supervised by Prof. Lord (Lionel) Tarassenko. During his DPhil, he designed, built, and open-sourced world-leading deep learning models for quantitative sleep monitoring from wearables. These are currently being evaluated in clinical studies for the longitudinal monitoring of linked health conditions including insomnia and depression. Alongside his DPhil, he has spent several years working in industry as an AI researcher/engineer at HealthTech companies LIO and Sanome. There he helped to develop several AI-enabled medical devices which have been deployed in healthcare systems in the UK and US. (Personal website: https://joml.io)
