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Date: December 15, 2021

Length: 60 minutes

Course Type: Archived Event

Instructor: Emma Pierson

Learning Level: Fundamental

Primary Audience: Researchers, PIs, Postdoctoral and Graduate Students, Other Study Team Members

Prerequisite: None

Skills Domain: Data Management and Informatics, Scientific Concepts and Research Design

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. The topic of this month's webinar was "Using Machine Learning to Increase Equality in Healthcare and Public Health."

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.

About the instructor

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.

Dates: Wednesday December 9th 12pm-1:00pm  
Location: Zoom  

Available courses

Date Location Type Price
2023 Course: Open January 1 through December 31 Online Archived Event This Course is Free