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Research

To prospective students: I welcome applications for PhD and Postdoctoral positions related to Axis 1 and 2 of my research program. Interested candidates are encouraged to apply through the McGill Biostatistics or Quantitative Life Sciences programs. I also host undergraduate students through specialized summer research programs. These opportunities typically involve collaborative group projects, offering hands-on experience in statistical genomics and computational methods.

Axis 1: Development of Statistical and Machine Learning Methods for Spatial Omics and Single-cell RNA-seq Data

Our research in spatial omics and single-cell RNA-seq focuses on integrating advanced statistical models and machine learning techniques to uncover spatially resolved gene expression patterns and infer cellular states. We aim to enhance spatial transcriptomic analyses, enabling better disease characterization, biomarker discovery, and precision medicine applications.

  • CeLEry: deep learning that leverages spatial transcriptomics to recover cell locations in single-cell RNA-seq data. (Nature Communications, 2023)
  • DiSTect: a Bayesian model for disease-associated gene discovery and prediction in spatial transcriptomics. (Bioinformatics, 2025)
  • Winnow-KAN: single-cell RNA-seq location recovery from small gene sets via a Kolmogorov-Arnold network. (BMC Bioinformatics, 2025)
  • SpaTM: topic models for inferring spatially informed transcriptional programs. (Briefings in Bioinformatics, 2025)
  • DenMark: a Bayesian hierarchical model for identifying cell-density-correlated genes in spatial transcriptomics. (preprint, bioRxiv 2026)
  • InSTaPath: integrating spatial transcriptomics and histopathology images via multimodal topic learning. (preprint, bioRxiv 2026)

Current Members

Mingchi Xu

Mingchi Xu
PhD Candidate

Qicheng Zhao

Qicheng Zhao
PhD Candidate

Weiyi Xiao

Weiyi Xiao
PhD Candidate

Yuyang Zhang

Yuyang Zhang
MSc Candidate

Yidan Cui

Yidan Cui
Postdoc

Alumni

Anji Deng

Anji Deng
MSc Non-thesis

Next Step: Data Analyst. Faculty of Nursing， University of Alberta
Qicheng Zhao

Qicheng Zhao
MSc Thesis

Kata Vuk

Kata Vuk
Postdoc Visiting

IVADO Visiting Scholar (Opliquepalm). Next Step: Postdoc in Machine Learning, University of Regensburg.

Axis 2: Methodology for Error-aware Statistical Inference in Modern Settings

As model-based decision and quantification increasingly impact biomedical research, we focus on developing robust statistical methodologies to ensure reliable inference. Our work addresses measurement error, misclassification, and uncertainty in data analysis to enhance interpretability and accuracy, particlar in machine learning and artificial Intelligence.

  • Generalized Network Structured Models (GNSM): network models with mixed responses subject to measurement error and misclassification. (Biometrics, 2023)
  • Generalized SIMEX: polynomial-extrapolation SIMEX for correcting covariate measurement error. (Statistics in Medicine, 2026)
  • augSIMEX: analysis of data with mixed measurement error and misclassification in covariates. (Journal of Statistical Computation and Simulation, 2019)
  • Zero-inflated Poisson with measurement error: Bayesian inference for zero-inflated counts measured with error. (Biometrics, 2023)

Current Members

Mincen Liu

Mincen Liu
PhD Candidate

Kejun Fang

Kejun Fang
Undergrad

Alumni

Cathy Shen

Cathy Shen
MSc Thesis

Next Step: Biostatistician at EVERSANA.
Kent Lu

Kent Lu
MSc Thesis

Next Step: Research Assistant, McGill University.
Jou-Chin Wu

Jou-Chin Wu
PhD Visiting

Next Step: Assistant Professor, National Central University (國立中央大學).

Axis 3: Statistics in Applicational and Translational Research

Our research extends to the application of statistical methods in real-world problems, including public health policy, pandemic modeling, and social data analysis. We integrate machine learning and statistics to derive meaningful insights from diverse data sources.

  • Sentiment Analysis: Examining COVID-19 tweets prior to the vaccine rollout to understand public perception and misinformation trends.
  • Model-based Forecasting: Predicting Canadian COVID-19 trends using statistical forecasting models to inform public health decision-making.
  • Effects of Non-Pharmaceutical Interventions: Estimating the impact of NPIs and mobility patterns on COVID-19 case trajectories in Ontario.

To prospective students interested in Axis 3: I currently only serve as a co-supervisor in Axis 3. If you are interested in working with me in this path, please identify a primary supervisor in Epidemiology or Public Health, and I would be happy to serve as a co-supervisor.

Alumni

Hani Rukh-E-Qamar

Hani Rukh-E-Qamar
MSc Thesis

Next Step: Medical Student, University of Saskatchewan.
Olivia Vaikla

Olivia Vaikla
MSc Thesis

Next Step: Clinical Research Educator at CANTRAIN.
Fio Vialard

Fio Vialard
MSc Thesis

Next Step: PhD Student at Barcelona Institute for Global Health.

Interested in our research? Email Me