Bailey Flanigan (she/her/hers) is a 4th year PhD student at Carnegie Mellon University, advised by Ariel Procaccia. She is interested in multiple areas of theory and AI, including social choice, econ-CS, algorithms, and learning theory.
Her recent research has focused primarily on designing sampling algorithms for selecting Citizens’ Assembly participants. This ongoing line of work addresses various aspects of this selection process, including its fairness, transparency, and manipulability. Recently, she has also been studying the related applications of democratic deliberation and polarization.
Beyond social choice, Bailey enjoys working on algorithms with learned predictions, beyond worst-case analysis, and other approaches that ask what is possible beyond classical impossibilities.
Outside of research, Bailey enjoys hiking, being outdoors, reading fiction, and cooking with (and for) friends.
- Data-Driven Decision Processes, Fall 2022. Visiting Graduate Student.