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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For our second talk in the series, we are delighted to welcome Dr Emma Pierson from Cornell University, who will be presenting on 'Using machine learning to increase equity in healthcare and public health'.

Abstract

Our society remains profoundly unequal. Worse, there is abundant evidence that algorithms can, improperly applied, exacerbate inequality in healthcare and other domains. This talk pursues a more optimistic counterpoint -- that data science and machine learning can also be used to illuminate and reduce inequity in healthcare and public health -- by presenting vignettes from domains including policing, women's health, and cancer risk scores.

Speaker Bio

Emma Pierson is an assistant professor of computer science at the Jacobs Technion-Cornell Institute at Cornell Tech and the Technion, and a computer science field member at Cornell University. She holds a secondary joint appointment as an Assistant Professor of Population Health Sciences at Weill Cornell Medical College. She develops data science and machine learning methods to study inequality and healthcare. Her work has been recognized by best paper, poster, and talk awards, an NSF CAREER award, a Rhodes Scholarship, Hertz Fellowship, Rising Star in EECS, MIT Technology Review 35 Innovators Under 35, and Forbes 30 Under 30 in Science. Her research has been published at venues including ICML, KDD, WWW, Nature, and Nature Medicine, and she has also written for The New York Times, FiveThirtyEight, Wired, and various other publications.

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