Faidra Monachou

Stanford University

Position: PhD Candidate
Rising Stars year of participation: 2021
Bio

Faidra Monachou is a final-year Ph.D. candidate in Management Science and Engineering at Stanford University. She is interested in market and information design with a particular focus on the role of discrimination diversity and information in education labor and sharing economy. Faidra’s research has been supported by various scholarships and fellowships from Stanford Data Science Stanford Human-centered AI Google and other organizations. She co-chaired the MD4SG’20 workshop and co-organizes the Stanford Data Science for Social Good program. Faidra received her undergraduate degree in Electrical and Computer Engineering from the National Technical University of Athens in Greece.

Discrimination, diversity, and information in selection problems

Discrimination, diversity, and information in selection problems
My research lies at the intersection of operations and social sciences. Using theory and data-driven simulations and combining tools and insights from operations economics computer science and sociology I study socioeconomic problems and policy questions that arise in markets and online platforms. My Ph.D. work looks at fundamental operational problems through a human-centered informational lens. From (i) combating discrimination and increasing diversity in decision-making systems (e.g. admissions policies in education hiring in online labor markets) to (ii) (re)designing markets for social good (e.g. public housing organ allocation) to (iii) optimizing online platforms (e.g. recommendation systems sharing economy) I use information design as a tool to build these systems in a fair equitable and efficient manner. More specifically a major line of my doctoral research has focused on discrimination diversity and information in selection problems. Motivated by recent decisions to suspend standardized testing in admissions we develop a theoretical Bayesian model to study the effect of dropping a feature (such as test scores) on academic merit and diversity and quantify the trade-off between information and access. We then build upon the previous framework to understand the role that differential privilege and the correlation between skill and privilege play in statistical discrimination.