Decoding health data

Bei Jiang uses statistical analysis and statistical machine learning to decode complex health data, searching for better patient outcomes. Her work has involved analyzing brain imaging, which could help build effective treatment plans for people with psychiatric disorders. Her research also focuses on integrating Bayesian modelling with statistical machine learning methods, aiming to overcome some of the roadblocks of classical statistical inference.

Jiang earned her MSc in Biostatistics at the University of Alberta in 2008 before completing her PhD at the University of Michigan. She returned to Edmonton in 2015 — first as an assistant professor and now an associate professor — at the U of A's Mathematical and Statistical Sciences faculty. In 2015, she was also named a research fellow with the U.S.-based Statistical and Applied Mathematical Sciences Institute.