Two of Amii’s researchers — Amii Fellow and Canada CIFAR AI Chairs Linglong Kong and Bei Jiang —  are receiving national recognition for their work in mathematics and statistics. 

This week, Kong was awarded with the CRM–SSC Prize in Statistics, one of Canada’s top prizes in the field while Jiang was honoured with an Emerging Leader Award from the Committee of Presidents of Statistical Societies.

CRM–SSC Prize in Statistics

The CRM–SSC Prize in Statistics is given out each year by the Centre de recherches mathématiques (CRM) and the Statistical Society of Canada (SSC). It recognizes the achievements of early-career statistical scientists, highlighting the “excellence and accomplishments in research during the first 15 years after their doctorate.”

The award organizers highlighted the breadth of Kong’s research, noting advancements in statistical machine learning, applying statistics to neuroimaging data, personalized medicine and distributional reinforcement learning. It also noted his work as an associate editor and guest editor for numerous journals in the field, including Journal of the American Statistical Association, The Canadian Journal of Statistics, and Frontiers in Neuroscience. “Linglong has risen to the top tier of mathematical statisticians and data scientists in Canada and internationally,” the SSC announcement notes. 

COPSS Emerging Leader Award

The COPSS Emerging Leader Award is also given out to early-career researchers who shape and strengthen statistics in Canada.Jiang was recognized for her groundbreaking contributions to statistical methodologies in trustworthy machine learning, strengthening the integration between statistics and machine learning with novel methodologies in neuroimaging data analysis, data privacy, and algorithmic fairness.

COPSS also noted Jiang’s outstanding mentorship of other statistical researchers, and her dedicated service to the statistical community.

Kong and Jiang are both prolific researchers and frequent collaborators. Some of their most recent research has involved developing methods to evaluate the reliability of reinforcement learning models in uncertain environments, as well as new approaches to preserving privacy when analyzing data in real time.