[Sds-seminars] Monday, Feb 24, Emma Pierson on "Data science methods to reduce inequality and improve healthcare"

[Dan Spielman]

S&DS|CS JOINT SEMINAR, EMMA PIERSONStanford University
Data science methods to reduce inequality and improve healthcare
Monday, February 24, 20204:00PM to 5:00PM
YINS see map
<http://maps.google.com/?q=17+Hillhouse+Avenue%2C+Rm.+328%2C+New+Haven%2C+CT%2C+06511%2C+us>

17 Hillhouse Avenue, Rm. 328
New Haven, CT 06511
Website <https://cs.stanford.edu/~emmap1/index.html>
Information and Abstract:

I will describe how to use data science methods to understand and reduce
inequality in two domains: criminal justice and healthcare. First, I will
discuss how to use Bayesian modeling to detect racial discrimination in
policing. Second, I will describe how to use machine learning to explain
racial and socioeconomic inequality in pain.

Bio: Emma Pierson is a PhD student in Computer Science at Stanford,
supported by Hertz and NDSEG Fellowships. Previously, she completed a
master’s degree in statistics at Oxford on a Rhodes Scholarship. She
develops statistical and machine learning methods to study two deeply
entwined problems: reducing inequality and improving healthcare. She also
writes about these topics for broader audiences in publications including
The New York Times, The Washington Post, FiveThirtyEight, and Wired. Her
work has been recognized by best paper (AISTATS 2018), best poster (ICML
Workshop on Computational Biology), and best talk (ISMB High Throughput
Sequencing Workshop) awards, and she has been named a Rising Star in EECS
and Forbes 30 Under 30 in Science.
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