CS Colloquium: Emma Pierson (Stanford) - Data Science Methods to Reduce Inequality and Improve Healthcare
Thu, Mar 05, 2020 @ 11:00 AM - 12:00 PM
Conferences, Lectures, & Seminars
Speaker: Emma Pierson, Stanford University
Talk Title: Data Science Methods to Reduce Inequality and Improve Healthcare
Series: CS Colloquium
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.
This lecture satisfies requirements for CSCI 591: Research Colloquium
Biography: 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.
Host: Bistra Dilkina
Location: Olin Hall of Engineering (OHE) - 132
Audiences: Everyone Is Invited
Contact: Assistant to CS chair