Professor Gronsbell develops statistical learning and inference methods that address key challenges of analyzing modern observational health data, including extreme missing data, complex measurement error, data heterogeneity, and bias and fairness. She has a decade of experience analyzing electronic health record (EHR) data in academia and industry. Her work has been published in the Journal of the Royal Statistical Society Series B, Biometrics, Statistics in Medicine, and the Journal of the American Medical Informatics Association.
Gronsbell also has extensive experience teaching courses across biostatistics, mathematical statistics, and data science. She is passionate about increasing diversity in STEM and runs a summer program that introduces high school students to machine learning, statistics, and programming through building a toy self-driving car.
Gronsbell received her B.A. in Applied Mathematics from UC Berkeley and her Ph.D. in Biostatistics from Harvard University. She completed her postdoctoral work in Biomedical Data Science at Stanford University and worked as a data scientist at Alphabet’s Verily Life Sciences before joining the University of Toronto.