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Bei Jiang is an Associate Professor in the Department of Mathematical and Statistical Sciences at the University of Alberta. He holds a Ph.D. in Biostatistics from the University of Michigan, Ann Arbor. His research is focused on developing statistical methods for analyzing longitudinal health outcome data, including Bayesian hierarchical modeling, mixture modeling, and functional imaging data analysis. He has contributed significantly to the field of statistical learning, specifically in kernel machine regression/classification and Bayesian support vector machines. Jiang has been recognized for his work with multiple awards, including the New Research Fellow accolade from SAMSI and the Alexander Graham Bell Canada Graduate Scholarship. He has held prominent research grants, such as the Achieving Fairness in AI Synthetic Data project funded by Amii and various collaborations aimed at improving statistical methods and machine learning applications. Jiang is also a CIFAR AI Chair and a Fellow of the Alberta Machine Intelligence Institute, reflecting his leadership in the AI research community.
University of Alberta • Edmonton, AB, Canada
Teaching and conducting research in biostatistics and machine learning.
Department: Mechanical Engineering and Engineering Management