Grace Yi

Grace-Yi-2024.jpg

Professor

Office: WSC 235
Tel: 519-661-2111 ext. 85762
Email:gyi5@uwo.ca

Personal Web Page
Research Group Page: covid-19-canada.uwo.ca

 

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 profession in various roles. She is currently a Co-Editor-in-Chief of The Electronic Journal of Statistics (2022-2024) and serves as the Editor of the Statistical Methodology and Theory Section for The New England Journal of Statistics in Data Science. Previously, she was the Editor-in-Chief of The Canadian Journal of Statistics (2016-2018) and took on the Presidency of the Statistical Society of Canada for the 2020-2022 term. Professor Yi founded the first chapter of the International Chinese Statistical Association in Canada (established in 2012) and chaired the Lifetime Data Science Section of the American Statistical Association in 2023.

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

Monographs

  1. G. Y. Yi (2017). Statistical Analysis with Measurement Error or Misclassification: Strategy, Method and Application, 479 pages. Springer Science+Business Media LLC, New York.
  2. G. Y. Yi, A. Delaigle, and P. Gustafson (2021). Handbook of Measurement Error Models, 577 pages. Edited volume for Chapman & Hall/CRC, Boca Raton, FL.
  3. N. Reid, C. Varin, and G. Y. Yi (2024+). Likelihood and its Extensions.  Chapman & Hall/CRC. To appear.

 

Refereed Publications

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

  1. Y. Bian∗, G. Y. Yi, and W. He (2024). A Unified Framework of Analyzing Missing Data with Regularized Likelihood. To appear in Computational Statistics and Data Analysis.
  2. Y. Sun∗∗ and G. Y. Yi (2024). Regression Learning with Limited Observations of Multivariate Responses and Features. The 2024 International Conference on Machine Learning (2024 ICML).
  3. Y. Khadem Charvadeh∗ and G. Y. Yi (2024). Q-Learning with Compound Outcome and Mixed Misclassification and Measurement Error in Covariates. To appear in Statistics in Biosciences.
  4. Y. Khadem Charvadeh∗ and G. Y. Yi (2024). Investigating Misclassification Effects on Optimizing Dynamic Treatment Regimes with Q-Learning. To appear in Statistics in Medicine.
  5. L.-P. Chen∗∗ and G. Y. Yi (2024). Censored Unbiased Boosting Estimation for Survival Data. To appear in Statistica Sinica.
  6. Y. Khadem Charvadeh∗ and G. Y. Yi (2024). Understanding Effective Virus Control Policies for COVID-19 with the Q-Learning Method. To appear in Statistics in Biosciences.
  7. Q. Zhang∗∗, G. Y. Yi, L.-P. Chen∗∗ and W. He (2023). Sentiment Analysis and Causal Learning of COVID-19 Tweets prior to the Rollout of Vaccines. PLOS ONE, 18(2): e0277878. https://doi.org/10.1371/journal.pone.0277878.
  8. H. Guo∗, B. Wang, and G. Y. Yi (2023). Label Correction of Crowdsourced Noisy Annotations with an
    Instance-Dependent Noise Transition Model. 2023 Neural Information Processing Systems (NeurIPS).
  9. L. Diao and G. Y. Yi (2023). Classification Trees with Misclassified Responses. Journal of Classification,
    40, 168-191.
  10. X. Liu∗∗, G. Y. Yi, G. Bauman, and W. He (2021). Ensembling Imbalanced-Spatial-Structured Support
    Vector Machine. Econometrics and Statistics, 17, 145-155.

Teaching

Courses taught in 2020/21:

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