Isabel is a second-year Information Science PhD student at Cornell Bowers College of Computing and Information Science, advised by Mor Naaman and Allison Koenecke. Her research uses data science, causal inference, and online experiments to audit algorithmic systems, with a particular focus on the effects of generative AI on information ecosystems and social evaluation. She is also a member of Cornell’s Artificial Intelligence, Policy, and Practice (AIPP) initiative.

Prior to Cornell, Isabel worked as a data scientist at Spotify, contributing to teams focused on algorithmic impact and responsibility and machine-learning engagement. She holds a B.S. in Statistics and Data Science from Yale, where her thesis examined assumptions in word-embedding debiasing methods and she contributed to research spanning STEM education, social networks, education, and child-welfare policy.