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 well as journals such as Nature Medicine, Nature Communications, AMIA, PLOS and JAIDS. Prior to joining Imperial College, Sonali was a postdoctoral research fellow at Harvard and a Swiss National Science Fellow. Sonali received her PhD (summa cum laude) in 2019 from the University of Basel, Switzerland where she built intelligent models for understanding the interplay between host and virus in the fight against HIV.

Apart from her research, Sonali is passionate about encouraging more discussion on the role of ethics in developing machine learning technologies to actively improve society.

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Please direct any questions about the webinar series to Brieuc Lehmann at b.lehmann@ucl.ac.uk.