Grace Yi

photo of Dr Yi.


Office: WSC 235
Tel: 519-661-2111 ext. 85762

Personal Web Page
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Grace Y. Yi is a professor at the University of Western Ontario where she currently holds a Tier I Canada Research Chair in Data Science. She is recognized as one of the influential women in Statistics.

Professor Yi received her Ph.D. in Statistics from the University of Toronto in 2000 and then joined the University of Waterloo as a postdoctoral fellow (2000-2001), Assistant Professor (2001-2004), Associate Professor (2004-2010), Professor (2010-2019), and University Research Chair (2011-2018). Professor Yi is a Fellow of the Institute of Mathematical Statistics (IMS), a Fellow of the American Statistical Association (ASA), and an Elected Member of the International Statistical Institute (ISI). In 2010, Professor Yi received the prestigious Centre de Recherches Mathmatiques and the Statistical Society of Canada (CRM-SSC) Prize, which recognizes a statistical scientist's excellence and accomplishments in research during the first fifteen years after earning their doctorate. Professor Yi was a recipient of the University Faculty Award (2004-2009) granted by the Natural Sciences and Engineering Research Council of Canada (NSERC). Her work with Xianming Tan and Runze Li won The Canadian Journal of Statistics Award in 2016.

Professor Yi has served the professions in various capacities. She was the Editor-in-Chief of The Canadian Journal of Statistics (2016-2018), and is currently the Editor of Statistical Methodology Section for The New England Journal of Statistics in Data Science. She was the President of the Biostatistics Section of the Statistical Society of Canada in 2016 and the Founder of the first chapter (Canada Chapter, established in 2012) of International Chinese Statistical Association. She takes on the Presidency of the Statistical Society of Canada for the period of 2020-2022.

Research Interests

Professor Yi's research interests focus on developing methodology to address various challenges concerning Data Science, public health, cancer research, epidemiological studies, environmental studies, and social science. Professor Yi's recent research has been centered around investigating machine learning and statistical methods to tackle problems concerning imaging data, missing data, measurement error in variables, causal inference, high dimensional data, survival data, and longitudinal data.

Selected Publications


  1. Y. Yi (2017). Statistical Analysis with Measurement Error or Misclassification: Strategy, Method and Application. Springer Science+Business Media LLC, New York.


  1. Y. Yi, A. Delaigle, and P. Gustafson (2021). Handbook of Measurement Error Models.  Chapman & Hall/CRC. In press. 


Selected Journal Publications

Students and post-doctoral fellows as co-authors are marked with * and **, respectively.


  1. Q. Zhang* and G. Y. Yi (2021). Marginal Analysis of Bivariate Mixed Responses with Measurement Error and Misclassification. To appear in Statistical Methods in Medical Research.
  2.  J. Fang* and G. Y. Yi (2021). Imputation and Likelihood Methods for Matrix-Variate Logistic Regression with Response Misclassification. To appear in The Canadian Journal of Statistics.
  3.  B. Zhao*, W. He, X. Liu**, and G. Y. Yi (2021). Dynamic Tilted Current Correlation for High-Dimensional Variable Screening. To appear in Journal of Multivariate Analysis.
  4.  Q. Zhang* and G. Y. Yi (2021). Genetic Association Studies with Bivariate Mixed Responses subject to Measurement Error and Misclassification. To appear in Statistics in Medicine.
  5.  L.-P. Chen* and G. Y. Yi (2021). Analysis of Noisy Survival Data with Graphical Proportional Hazards Measurement Error Models. To appear in Biometrics.
  6.  J. Fang* and G. Y. Yi (2021). Matrix-variate Logistic Regression with Measurement Error. To appear in Biometrika.
  7.  X. Liu**, G. Y. Yi, G. Bauman, and W. He (2021). Ensembling Imbalanced-Spatial-Structured Support Vector Machine. Econometrics and Statistics, 17, 145-155.
  8.  Y. Khadem Charvadeh*  and G. Y. Yi (2020). Data Visualization and Descriptive Analysis for Understanding Epidemiological Characteristics of COVID-19: A Case Study of a Dataset from January 22, 2020 to March 29, 2020. The Journal of Data Science, 18(3), 526-535.
  9.  D. Shu_ and G. Y. Yi (2020). Causal Inference with Noisy Data: Bias Analysis and Estimation Approaches to Simultaneously Addressing Missingness and Misclassification in Binary Outcomes. Statistics in Medicine, 39, 456-468.
  10.  S. Barui**  and G. Y. Yi (2020). Semiparametric Methods for Survival Data with Measurement Error under Additive Hazards Cure Rate Models. Lifetime Data Analysis, 26, 421-450.


Courses taught in 2020/21:

  • SS9878/CS9878 (Analysis of High Dimensional Noisy Data)