2025 CRM-SSC Prize awarded to Linglong Kong (University of Alberta)

Born in 1978, Linglong Kong grew up in the village of Xuchang in Henan province, China. He studied Probability and Statistics at Beijing Normal University, where he obtained his Bachelor’s degree in Statistics in 1999. He then completed his Master’s degree in Statistics at Peking University in 2002 followed by a PhD at the University of Alberta, where his PhD dissertation On multivariate quantile regression: directional approach and application with growth charts was supervised by Professor Ivan Mizera. Linglong held postdoctoral positions at Michigan State University, and the University of North Carolina (Chapel Hill).

Since he joined the faculty of the University of Alberta in 2012, Professor Kong’s record has been nothing less than breathtaking. At this writing his CV shows over 80 published or ‘in press’ refereed journal papers in first-rate outlets, and over 40 refereed papers presented at conferences with generally very low acceptance rates. His mentorship has been outstanding — at last count, he is supervising 8 post-docs, 15 PhD students, and 6 MSc students. He has graduated a multitude of others who are now pursuing successful careers of their own in research or the commercial sector. A letter writer says “His mentorship fosters independence, inclusivity, and a culture of excellence. He has allowed his students to engage in high-profile research projects, resulting in numerous coauthored publications in top-tier journals and conferences. This dedication has cultivated a new generation of statistical scientists who continue to advance the field. His trainees’ successes reflect his exceptional ability to inspire and nurture talent; their achievements are a testament to his commitment to their professional growth.”

Professor Kong has made outstanding contributions to the profession in the form of editorial work. He is Associate Editor at each of Journal of the American Statistical Association, Annals of Applied Statistics, International Journal of Imaging Systems and Technology, Statistics and Its Interface and The Canadian Journal of Statistics (CJS) and was a Guest Editor of a special issue on neuroimaging data analysis in CJS as well as Guest Associate Editor at Frontiers in Neuroscience and he became Fellow of the American Statistical Association in 2025.

He is an internationally recognized researcher in statistical machine learning and statistical optimization — he is AI chair in the Canadian Institute for Advanced Research (CIFAR), for which he is based at the Alberta Machine Intelligence Institute where he is a Fellow. This follows his 2020 appointment as Canada Research Chair in Statistical Learning, based on his work in neuroimaging data analysis, with contributions to ensemble and hierarchical modelling, matrix factorization, and distributional reinforcement learning. More recently, he has “pioneered privacy-preserving methods under Local Differential Privacy and extended privacy frameworks to Riemannian manifolds, safeguarding sensitive data in fields such as medical imaging and health care analytics.”

Of his work as a whole, a writer says that Linglong has “pioneered statistical methods in neuroimaging data analysis that integrate spatial, functional, and high-dimensional data, enabling ground breaking insights into brain structure and function.” He goes on to say “Dr. Kong’s contributions to trustworthy machine learning address some of the most pressing challenges in AI, including fairness and privacy. His work on conformalized fairness via quantile regression and Gaussian differential privacy on Riemannian manifolds exemplifies his ability to combine rigorous statistical theory with impactful, ethical applications. These contributions are critical for developing AI systems that are equitable, reliable, and aligned with societal values.”