Emma Pierson
Title: Using new data to answer old questions
Abstract: The explosion of new data sources has created new opportunities, and necessitated new machine learning methods, to answer old questions in the health and social sciences. This talk discusses three stories under this theme: first, usingimage data to quantify inequality in policing; second, usingtext data to interpretably predict target variables and characterize disparities; and third, usingaddress data to infer fine-grained migration patterns.
Bio: Emma Pierson is an assistant professor of computer science at UC Berkeley and core faculty in the Computational Precision Health program. 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, Forbes 30 Under 30 in Science, AI2050 Early Career Fellowship, and Samsung AI Researcher of the Year. Her research has been published in venues includingNature, JAMA, The New England Journal of Medicine, PNAS, Nature Medicine, ICML andICLR, and she has also written forThe New York Times, FiveThirtyEight, Wired, and various other publications.
