Pierre Robillard Award 2017; Andy Leung
Andy’s research filled an important gap in the development of new robustness models and tools to deal with outliers in highly multivariate data. When the number of variables becomes very large relative to the sample size, the classical robustness model becomes unrealistic, unappealing and rather unsatisfactory. Andy’s work addresses the limitations of the classical model and proposes a new generation of estimators which are robust against a wider spectrum of data contamination.
Andy grew up in the Vancouver area. He received his BSc degree in Mathematics and Statistics at the University of British Columbia in 2011. He stayed on to do his MSc in Statistics, but very soon transferred to the PhD program. During his graduate studies he also worked as a data analyst for the Ovarian Cancer in Alberta and British Columbia (OVAL-BC) study at the BC Cancer Agency. In the last year of his doctoral studies, Andy joined Ecoation, a Vancouver-based AgTech startup company, where he now works as the lead data scientist.
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.
Andy Leung will present an overview of his work in a special session at this year’s SSC Annual Meeting at the University of Manitoba.
The citation for the award reads:
"To Andy Leung, for the thesis entitled “Robust Estimation and Inference under Cellwise and Casewise Contamination”.
Thanks to Ruben Zamar, who was primarily responsible for producing this material.