Data Science/Computational Social Science Seminar: Emma Pierson

Using machine learning to increase quality 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 inequality in healthcare and public health – by presenting vignettes about women's health, COVID-19 and pain.

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, 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, AISTATS, KDD, WWW, CSCW, Nature, and Nature Medicine, and she has also written for The New York Times, FiveThirtyEight, The Atlantic, The Washington Post, Wired, and various other publications.

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