The CRM-SCC 2023 Prize is awarded to Professor Zhou Zhou of the Department of Statistical Sciences at the University of Toronto. This award recognizes his fundamental contributions to time series analysis and nonparametric statistics, including non-stationary time series, nonlinear time series, time-frequency analysis, Gaussian approximations, resampling methods, and sieve inference for complex dependent data, as well as robust change point detection for non-stationary data.
Zhou Zhou was born and raised in Hunan Province, China. He studied mathematics at the prestigious Peking University, where he received his bachelor’s degree in 2003. He then enrolled in statistics at the University of Chicago, where he received his PhD in 2009 under the supervision of Wei-Biao Wu. He then joined the Department of Statistical Sciences at the University of Toronto, where he quickly rose from Assistant Professor to Associate Professor in 2015 and then to Full Professor in 2021.
Zhou is a prolific, original and independent researcher in the field of time series analysis and nonparametric statistics. He has an impressive research record, with 31 research papers published or accepted in top journals, including leading journals such as Annals of Statistics (6 times), JRSSB (5 times) and JASA (2 times). He received the NSERC Discovery Accelerator Supplement in 2021 in recognition of his outstanding research contributions and very promising future.
Zhou has made many fundamental contributions to the analysis of non-stationary time series, an increasingly important area when studying long series with complicated structures. Zhou and Wu (Ann Stat 2009) proposed a framework for analyzing locally stationary (LS) time series from the perspective of nonlinear physical systems, where local stationarity refers to a special type of non-stationarity with smoothly varying data generation mechanisms. Zhou (JASA 2013) proposed a class of piecewise locally stationary (PLS) time series models that generalize this framework by allowing jumps or abrupt changes in the data generation mechanism. Both of these works have become important tools for researchers in this area and demonstrate high generality, mathematical depth and practical relevance. Zhou, his trainees and collaborators have subsequently made significant progress in developing theory and methodology for non-stationary time series analysis in LS and PLS frameworks. A series of papers by Zhou and Wu established a systematic statistical theory for nonparametric conditional quantile inference of non-stationary processes. In addition, Ding and Zhou (Ann Stat 2020) and Cui, Levine, and Zhou (EJS 2021) have established fundamental results on statistical inference of covariance structures of locally stationary time series.
Zhou is also a world-renowned expert in change point detection, an area of statistics that has skyrocketed in popularity over the past two decades due to its applications. Conventional theory and algorithms for change-point detection generally assume that noise disturbances are i.i.d. or stationary. In a pioneering paper published in JASA in 2013, Zhou demonstrated the loss of accuracy of a large class of classical change point detection algorithms in the presence of non-stationary noise, and proposed a simple and elegant bootstrap algorithm for change point testing that is robust over a large class of non-stationary noise. This robust change point detection framework was later generalized to constrained regression in Zhou (JRSSB 2015) and to M-regression in Wu and Zhou (Ann Stat 2018). Zhou is also a pioneer in simultaneous nonparametric inference for time-dependent data. And recently, he has made breakthroughs in simultaneous nonparametric inference by sieving dependent data.
Zhou is not only an outstanding researcher, but also a prolific mentor and supervisor. He has graduated seven PhD students and is currently supervising four others. He provides editorial services for journals such as Bernoulli and Statistics and Probability Letters. He has also organized invited sessions at several conferences. His involvement in the community, his extensive high-quality research, and his many doctoral students amply demonstrate his leadership role in the field.
When not working, Zhou relaxes with his wife Wendy Liu and their young children (ages 5 and 2), watches science fiction movies, and follows the ups and downs of the major European soccer leagues.
The CRM-SSC Prize
The CRM-SSC Prize in Statistics recognizes the excellence and research achievements of a statistician during the fifteen years following the award of his or her doctorate (or equivalent degree). It is awarded annually by the Centre de recherches mathématiques and the Statistical Society of Canada.