How can data science and machine learning address healthcare inequality?

This seminar from the Center for Quantitative Methods and Data Sciences (QM&DS), in partnership with the Biostatistics, Epidemiology and Research Design (BERD) Center at Tufts CTSI and the Data-Intensive Studies Center (DISC) at Tufts University, occurred on Wednesday, December 15 2-3:30pm via Zoom.

Our society remains profoundly unequal. Worse, there is abundant evidence that algorithms can, improperly applied, exacerbate inequality in healthcare and other domains. This recorded lecture 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.

Featured Speaker

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 develops data science and machine learning methods to study inequality and healthcare. Her work has been recognized by a Rhodes Scholarship, Hertz Fellowship, Rising Star in EECS, MIT Technology Review 35 Innovators Under 35, and Forbes 30 Under 30 in Science. She has written for The New York Times, FiveThirtyEight, The Atlantic, The Washington Post, Wired, and various other publications.