Advances and Challenges in Machine Learning for healthcare
Speaker: Danielle Belgrave (Microsoft)
Venue: MANDEC Lecture Theatre, 3rd Floor, University Dental Hospital, Higher Cambridge Street, Manchester, M15 6FH
Abstract: Health presents one of the most challenging domains of machine learning research. This offers an exciting opportunity for machine learning techniques to impact healthcare in a meaningful way. In this talk, we will look at some of the key challenges in machine learning for healthcare and some of the advances which have been made towards addressing these challenges.
Bio: Danielle Belgrave is a machine learning researcher in the Healthcare Research Group at Microsoft Research. Her research focuses on integrating medical domain knowledge to develop statistical machine learning models to understand disease progression and heterogeneity. Danielle's main research interests are in probabilistic graphical modelling and causal modelling frameworks to identify subtypes of disease, in order to help develop personalized treatment and intervention strategies. 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 Medical Research Council Fellow at Imperial College London. A full list Danielle Belgrave's publications can be found here.