Machine learning advances are opening new routes to more precise healthcare, from the discovery of disease subtypes for stratified interventions to the development of personalized interactions supporting self-care between clinic visits. This offers an exciting opportunity for machine learning techniques to impact healthcare in a meaningful way. Within the healthcare domain, machine learning for mental healthcare is an under-investigated area and yet a potentially highly impactful area of research. In this talk, Danielle Belgrave from the Healthcare Intelligence Group at Microsoft Research in Cambridge (UK) will present recent work on machine learning to enable a more personalized approach to mental healthcare, whereby information can be aggregated from multiple sources within a unified modelling framework. She will present applications from both mental health and respiratory diseases.

Danielle Belgrave is a machine learning researcher in the Healthcare Intelligence group at Microsoft Research, in Cambridge (UK) where she works on Project Talia. Her research focuses on integrating medical domain knowledge, probabilistic graphical modelling and causal modelling frameworks to help develop personalized treatment and intervention strategies for mental health. Mental health presents one of the most challenging and under-investigated domains of machine learning research. In Project Talia, she and her team explore how a human-centric approach to machine learning can meaningfully assist in the detection, diagnosis, monitoring, and treatment of mental health problems. She obtained a BSc in Mathematics and Statistics from London School of Economics, an MSc in Statistics from University College London and a PhD in the area of machine learning in health applications from the University of Manchester. Prior to joining Microsoft, she was a tenured Research Fellow at Imperial College London.

This seminar is part of the Center for Research on Computation and Society’s AI for Social Impact seminar series. The event is free and open to the public. Register for the series at url.seas.harvard.edu/aisi