Emilio is a data scientist on the Research Computing and Data Services team, working with researchers across the university to advance data science projects and provide consulting. He also teaches workshops in SQL, Python, and R. His technical skills include web scraping, natural language processing (NLP), supervised machine learning, and statistical modeling of survey data. He has collaborated on projects such as scraping job postings from diverse sources using local, high-performance computing (HPC), and cloud resources; extracting information with NLP techniques (including rules, entity recognition models, and large language models); and processing over 410,000 PDF documents on an HPC cluster.
He holds a PhD in sociology and an MS in statistics from Northwestern University, with a research focus on the sociology of data—investigating the economic dynamics of machine learning, privacy, and 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 also has a BA and MA in law, enriching his interdisciplinary interests and collaboration across various research domains. He has reviewed data science book proposals for Manning Publications and completed internships at Meta and Slack, where he gained product analytics experience. Emilio is passionate about mindfulness and teaches yoga in his spare time.