Post-Graduate Stories

Yidan Cui Will Focus on “Advancing Statistical Inference in Spatial Genomics: Methods for Multi-Slice Data”

As a 2026 CANSSI-StatLab Distinguished Postdoctoral Fellow, Yidan Cui will take part in a comprehensive program that involves teaching, interdisciplinary or applied collaboration, professional development, and a research project focusing on the development of new statistical frameworks for analyzing multi-slice spatial genomics data. Yidan will work under the supervision of Professor Dehan Kong (University of Toronto) and Professor Qihuang Zhang (McGill University).

Program: CANSSI-StatLab Distinguished Postdoctoral Fellowship
Region: National
Date: 2026–2028

Project Focus Areas

This project develops new statistical frameworks for analyzing multi-slice spatial genomics data, where high-dimensional molecular features are measured across consecutive tissue sections. A key objective is to reconstruct latent spatial coordinates that capture smooth tissue variation, using modern approaches such as optimal transport, and to align these coordinates with physical tissue structure. Building on this representation, the project will advance methods for model calibration, uncertainty quantification, and imputation, enabling rigorous statistical inference on perturbation effects in gene expression and related causal questions. Yidan will contribute to both methodology and theory, while also engaging in applied work through open-source R/Python software development and real-data analyses in spatial transcriptomics, with a focus on neurodevelopmental disorders.

In particular, she will work at the interface of statistics, machine learning, and biomedicine to translate manifold learning on distributions into practical 3D reconstruction tools for spatial transcriptomics and will use statistical methods to discover biological insights, with applications focused on neurodevelopmental disorders (e.g., autism).

Getting to Know Yidan

Yidan Cui completed her PhD in Biostatistics in the Department of Bioinformatics and Biostatistics at Shanghai Jiao Tong University under the supervision of Professor Zhangsheng Yu. Her doctoral research developed three statistical frameworks for analyzing high-dimensional omics data in large-scale cohort studies, which have been rigorously evaluated through simulations and applied to real datasets.

Her long-term goal is to establish an independent academic research program focused on statistical methods for high-dimensional omics data and to develop robust methodologies that not only elucidate the molecular mechanisms underlying complex traits but also bridge the gap between omics data and clinical utility in precision medicine.

She sees the CANSSI-StatLab Distinguished Postdoctoral Fellowship as an ideal environment to support these goals and her desire to work at the intersection of statistical methods and clinical application. She also notes that the training offered by the program in teaching, mentoring, and scientific communication will provide essential preparation for a future faculty role.

I view this fellowship as the essential bridge between my doctoral training and a successful, independent research career in statistical science.