Junhan Fang is the winner of the Pierre Robillard Award of the Statistical Society of Canada in 2021. Junhan’s PhD thesis, entitled “Matrix-Variate Regression with Measurement Error", was written while she was a doctoral student at the University of Waterloo, working under the supervision of Grace Y. Yi who is currently at the University of Western Ontario.

Junhan is currently holding a post-doctoral fellowship at Yale University, USA. She has an MSc degree in Biostatistics from Emory University, USA (2016) and a BA degree in Statistics from The Southwestern University of Finance and Economics, China (2014).

Junhan's thesis research focuses on investigating problems concerning matrix-variate data that are subject to measurement error or misclassification. The contributions include quantifying biases induced from ignoring measurement error in covariates and developing correction methods to adjust for measurement error effects, studying the effects of mismeasurement in responses on inference under matrix-variate logistic regression, and exploring regularization methods for handling high-dimensional error-contaminated data with sparsity.  Imputation and likelihood-based methods as well as Bayesian methods are proposed in her research. Her developed methods have been applied to analyze real-world data, including a breast cancer prognostic dataset, an electroencephalography (EEG) imaging dataset, and a voice treatment companion dataset.

The criteria used in selecting the winner of the Pierre Robillard Award include the originality of ideas and techniques, the possible applications and their treatment, and the potential impact of the work. The award is named in memory of Professor Pierre Robillard, an outstanding dynamic young statistician at the Université de Montréal, whose untimely death in 1975 cut short what promised to be a highly distinguished career.

Junhan will present an overview of her work at a special session at this year’s SSC virtual Annual Meeting.