Peijun Sang, Pierre Robillard Award 2019
This prize recognizes the best PhD thesis in probability or statistics defended at a Canadian university in a given year.
Peijun Sang is the winner of the Pierre Robillard Award of the Statistical Society of Canada. Peijun’s thesis, entitled “New Methods and Models in Functional Data Analysis" was written while he was a doctoral student at the Simon Fraser University, working under the supervision of Jiguo Cao.
Peijun joined the Department of Statistics and Actuarial Science at the University of Waterloo in September 2018 as an assistant professor.
His current research interests are focused on functional data analysis methods. Data from electroencephalogram signals, function magnetic resonance imaging and diffusion tensor imaging are important examples. He is interested in applying functional data analysis techniques to study functional connectivity between imaging data collected from different regions of the brain. He is concerned with large sample properties of high dimensional functional regression models that have been proposed for this type of data. He is also interested in dependence modelling with copulas for discrete and time-to-event outcomes.
In July 2010, Peijun earned a B.Sc. degree in Statistics from Zhejiang University in Hangzhou, China and then, in August 2014, he earned an M.Sc. degree in Statistics from the University of British Columbia under the supervision of Harry Joe.
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.
Peijun Sang will present an overview of his work in a special session at this year’s SSC Annual Meeting at the University of Calgary.
The citation for the award reads:
"To Peijun Sang, for the thesis entitled “New Methods and Models in Functional Data Analysis”