Information for Prospective Students

My research activities are computationally intensive, aiming to develop new data analysis software and solve real life science problems. Therefore, I demand that my students have been trained sufficiently in computer programmings, such as in R, Python, and C++, and also have strong backgrounds in mathematics and statistics. It is not required that students come with all of these skills. However, they are expected to demonstrate abilities and enthusiasm to grasp these skills.

Considering the big number of inquiries I receive, I only reply to the students that of my interest after reading their profiles.

Graduate students

1. Hao Hu, Ph.D. September 2021 - present, co-supervised with Prof. Li Xing

2. Lina Li, M.Sc, May 2021 to September 2021, MITACS project supervisor

   Publication: Real-time estimates of the reproduction rate (R_t) of Canadian provinces: https://longhaisk.github.io/CanadaCovidRt/

3. Hao Hu, M.Sc. September 2020 - August 2021, co-supervised with Prof. Li Xing, transferred to Ph.D. program

4. Wutao Yin, PhD September 2019 - December 2021, co-supervised with Prof. Fang-Xiang Wu

   Project: Deep learning for fMRI medical data

   Paper: Yin, W., Li, L., Wu, F.-X., 2020. Deep learning for brain disorder diagnosis based on fMRI images. Neurocomputing.

5. Tingxuan Wu, PhD January 2019-present, co-supervised with Prof. Cindy Feng

   Project: Bayesian Randomized Survival Probability Residual for Spatial-Temporal Data with Censoring

6. Man Chen, M.Sc. 2021.

   Thesis: Association between Gut Microbiome and Parkinson's Disease Revealed by Sparse Learning.

   Paper: Dong M, Li L, Chen M, Kusalik A, Xu W. Predictive analysis methods for human microbiome data with application to Parkinson’s disease. PLOS ONE. 2020 Aug 24;15(8):e0237779.

7. Mei Dong, M.Sc., 2019, co-supervised with Prof. Lloyd Balbuena

   Thesis: Feature Selection Bias in Assessing the Predictivity of SNPs for Alzheimer's Disease

   Employment: Senior Research Analyst at the University of Toronto.

   Paper: Dong M, Li L, Chen M, Kusalik A, Xu W. Predictive analysis methods for human microbiome data with application to Parkinson’s disease. PLOS ONE. 2020 Aug 24;15(8):e0237779.

   Bai, W., Dong, M., Li, L., Feng, C., Xu, W., 2021. Randomized quantile residuals for diagnosing zero-inflated generalized linear mixed models with applications to microbiome count data. BMC Bioinformatics 22, 564.

8. Xiaoying Wang, M.Sc., 2019

   Thesis: Comparison of Statistical Testing and Predictive Analysis Methods for Feature Selection in Zero-inflated Microbiome Data

9. Tingxuan Wu, M.Sc. in biostatistics, 2018, co-supervised with Prof. Cindy Feng

   Thesis: Randomized Survival Probability Residuals for Assessing Parametric Survival Models

   Employment: PhD student at the University of Saskatchewan

   Paper: Li, L., Wu, T., Feng, C. Model Diagnostics for Censored Regression via Randomized Survival Probabilities. Statistics in Medicine, 2021. a limited access link to the paper; arxiv preprint; R functions for computing NRSP residuals for survreg and coxph objects with animated demonstration.

10. Wei Bai, M.Sc., 2018, co-supervised with Prof. Cindy Feng

    Thesis: Randomized Quantile Residual for Assessing Generalized Linear Mixed Models with Application to Zero-Inflated Microbiome Data

    Employment: Statistical Programmer, Everest Clinical Research, Markham, Ontario.

    Paper:Bai, W., Dong, M., Li, L., Feng, C., Xu, W., 2021. Randomized quantile residuals for diagnosing zero-inflated generalized linear mixed models with applications to microbiome count data. BMC Bioinformatics 22, 564.

11. Arash Shamloo, M.MATH (project-based), 2017

    Project: Randomized quantile residuals for accelerated failure time models

    Employment: Research assistant, College of Pharmacy and Nutrition, University of Saskatchewan.

12. Yunyang Wang, M.Sc., 2016

    Thesis: Comparison of Stochastic Volatility Models Using Integrated Information Criteria

    Employments: Statistician at Montreal office of Evidera (a PPD company), Montreal, QC; Intern at the PathWise Solutions, AON Securities, Toronto.

13. Alireza Sadeghpour, M.Sc., 2016, co-supervised with Prof. Cindy Feng

    Thesis: Empirical Investigation of Randomized Quantile Residuals for Diagnosis of Non-Normal Regression Models

    Employment: Statistician at Health Canada, Ottawa.

    Paper: Feng C, Li L, Sadeghpour A. A comparison of residual diagnosis tools for diagnosing regression models for count data. BMC Medical Research Methodology. 2020 Jul 1;20(1):175.

14. Naorin Islam, M.Sc., 2016, co-supervised wtih Prof. Khan

    Thesis: Substance Abuse and Health: A Structural Equation Modeling Approach to Assess Latent Health Effects

    Employment: Research assistant, College of Pharmacy and Nutrition, University of Saskatchewan.

15. Lai Jiang, Ph.D., 2015

    Thesis: Fully Bayesian T-probit Regression with Heavy-tailed Priors for Selection in High-Dimensional Features with Grouping Structure

    Employment: Postdoctoral fellow at Lady Davis Institute, Jewish General Hospital, McGill University in Montreal.

    Paper: Jiang L, Greenwood CMT, Yao W, Li L. Bayesian Hyper-LASSO Classification for Feature Selection with Application to Endometrial Cancer RNA-seq Data. Scientific Reports. 2020 Jun 16;10(1):9747.

16. Shi Qiu, M.Sc., 2015 (co-supervised with Cindy X. Feng)

    Thesis: Cross-validatory Model Comparison and Divergent Regions Detection using iIS and iWAIC for Disease Mapping

    Employment: "Data Service Specialist" at IRD Inc. in Saskatoon.

    Papers:

    • Longhai Li, Cindy X. Feng, and Shi Qiu* (2017), Estimating Cross-validatory Predictive P-values with Integrated Importance Sampling for Disease Mapping Models. [slides], [R code], arXiv:1603.07668, Statistics in Medicine, Volume 36, Issue 14, Pages 2220-2236.

    • Li, L., Qiu, S.*, Zhang, B.*, and Feng, C.X. (2016). Approximating Cross-validatory Predictive Evaluation in Bayesian Latent Variables Models with Integrated IS and WAIC. arXiv 1404.2918, [slides-I], [slides-II], Statistics and Computing, Volume 26, Issue 4, pp 881-897.

17. Masud Rana, M.Sc., 2012 (co-supervised with Prof. Shahed Khan)

    Thesis: Spatial-Longitudinal Bent-Cable Model with an Application to Atmospheric CFC Data

    Paper: A Statistical Investigation to Monitor and Understand Atmospheric CFC Decline with the Spatial-Longitudinal Bent-Cable Model.

    Employments: Biostatistician at Clinical Research Support Unit by College of Medicine of University of Saskatchewan.

18. Zhengrong Li, M.Sc., 2012.

    Thesis: A Non-MCMC Procedure for Fitting Dirichlet Process Mixture Models

    Employments: Data Service Specialist at IRD Inc. in Saskatoon.

Undergraduate Students

1. Lina Li, undergraduate research assistant, 2020

   Project title: Investigation of Randomized Quantile Residuals

2. Xinyu Liu, undergraduate research assistant, 2019

   Project title: Development of an R package for Bayesian Hyper-LASSO Logistic Regression

   Software:
   HTLR: Bayesian Logistic Regression with Heavy-Tailed Priors, [CRAN page], [Github page]. HTLR was listed in the top 40 new packages in October 2019 by R views of Rstudio.

3. Jian Su, undergraduate research assistant, 2018

   Project title: Development of Bayes Predictive Models based on Generalized Linear Mixed Models with application to microbiome data

4. Jiaqi Xiao, undergraduate research assistant, 2015

   Project title: Application of iIS and iWAIC to Compare Models for Economic Data.

   Employment: M.Sc. student at Queen's University

5. Zhouji Zheng, undergraduate research assistant, 2014 and 2015

   Project title: Application of Importance Sampling to Compare Bayesian Stochastic Volatility Models.

   Employment: M.Sc. student at the University of Toronto

6. Bei Zhang, undergraduate research assistant, 2013

   Project title: Cross-validatory Model Checks with iIS for Logistic Random Effect Models

   Paper: Approximating Cross-validatory Predictive Evaluation in Bayesian Latent Variables Models with Integrated IS and WAIC.