معرفی
Jessica Gronsbell is an Assistant Professor at the University of Toronto in the Department of Statistical Sciences. Her work focuses on developing statistical and machine learning methods for electronic health records (EHRs) and mobile health (mHealth) data, addressing challenges like measurement error, missing data, and fairness in algorithmic applications.
- BA in Applied Mathematics, UC Berkeley
- PhD in Biostatistics, Harvard University
- Postdoctoral work, Department of Biomedical Data Science, Stanford School of Medicine
Her research spans semi-supervised learning, data integration, and critical data studies, with applications in EHRs, biobanks, and mobile health technologies. Recent work includes evaluating fairness in semi-supervised settings and improving statistical inference with machine learning-derived data.
Current trends in her publications reflect a focus on machine learning ethics, health disparities, and computational methods in biostatistics. She actively mentors students, including participation in the Florence Nightingale Day, and contributes to open-source tools like the fairness evaluation R package on CRAN.
