Jesse Gronsbell is an Associate Professor in Statistical Sciences at the University of Toronto. Her research focuses on developing statistical learning and inference methods to address key challenges in analyzing modern observational health data, including extreme missing data, complex measurement error, issues of bias and fairness, and the use and implications of AI generated data. She also works in critical data studies, with a focus on the challenges of collecting sex and gender data in electronic health records.