The Statistical Methods for Health Equity Series is a monthly online series co-hosted by the Data Science for Health Equity community, the Alan Turing Institute, and the Department of Statistical Science at University College London.

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To kick-off this series we are delighted to welcome Dr Sonali Parbhoo from Imperial College London, who will be presenting 'On addressing bias and uncertainty for equity in healthcare'.

Sonali is an Assistant Professor and leader of the AI for Actionable Impact (AI4AI) lab at Imperial College London. Her research focuses on sequential decision-making in uncertainty, causal inference and building interpretable models to improve clinical care and deepen our understanding of human health, with applications in areas such as HIV and critical care.

She was recently named a Rising Star in AI in 2021. Her work has been published at a number of machine learning conferences such as NeurIPS, AAAI, ICML and AISTATS as…