Emilio is a data scientist in the Research Computing and Data Services team. He works with researchers and staff across the university to support data science projects and teach workshops on programming and statistics. He has experience in data visualization and using statistical and predictive modeling on survey data.
Emilio has a background in both studying and using data science. His PhD research focused on the sociology of data, including the economic dynamics behind the rise of machine learning, the increasing demand for privacy, and perceptions of algorithmic fairness. For his dissertation, Emilio investigated how companies have used machine learning since the mid-1990s, as well as the background behind popular open-source libraries. He also studied how Americans perceive the fairness of predictive automation in the context of credit scores, criminal justice, and hiring using an inductive, predictive approach.
Emilio completed two internships at Meta and Slack, where he learned about product analytics. In the former, he used regression analysis to identify the main user segment for augmented reality effects created by businesses. In the latter, he explored and visualized log data to design a new metric measuring how proficient users are at navigating Slack. Emilio taught a summer course on supervised learning for the social sciences at Universidad de los Andes (Bogotá, Colombia) and teaches workshops for RCDS on SQL, Python, R, and more.
Emilio has a PhD in sociology and an MS in statistics, both from Northwestern University. Before that, he obtained a BA and MA in law. As a result, he has multiple interests and loves working with diverse researchers across disciplines. Outside of work, Emilio is passionate about mindfulness.