The Campbell Lab is a group of data scientists working at the interface of statistics, machine learning, and translational biomedicine. We are based in Toronto's Discovery District at the Lunenfeld-Tanenbaum Research Institute of Sinai Health System and the Departments of Molecular Genetics and Statistical Sciences at the University of Toronto.
We have a number of research focusses including:
- Computational modelling of single-cell and spatially resolved ‘omics data
- Understanding the composition and dynamics of the tumour microenvironment and how it enables tumour progression
- Machine learning algorithms to help automate biological and biomedical data analysis
Latest news
Mar 5, 2024
New preprint on cell segmentation aware clustering for spatial expression assays
Our preprint on STARLING - a new method for clustering highly multiplexed imaging data while accounting for segmentation errors - is now up on bioRxiv along with a corresponding twitter thread.
Mar 1, 2024
New paper on single-cell data integration in imbalanced settings
Congratulations to Hassaan, Michael, and Chengxin whose paper understanding the impacts of dataset imbalanced on single-cell data integration is now published in Nature Biotechnology.
Feb 3, 2024
New paper on active and self-supervised learning for single-cell expression data
Congratulations to Michael and Sean whose work understanding the impact of active and self-supervised learning on efficient annotation of single-cell expression data has been published in Nature Communications.
Jan 16, 2024
Welcome to Sarah
Welcome to Sarah Asbury who joins as a graduate student developing methods to map T cell exhaustion from multi-modal data.
Jan 9, 2024
New preprint on automated machine learning for scRNA-seq analysis
Congratulations to Cindy and Alina for their new preprint on automated machine learning for scRNA-seq analysis!.
Sep 12, 2023
Scholarship successes
Congratulations to Shanza for being awarded a 3 year Canada Graduate Scholarships — Doctoral award and to Michael for being awarded a TFRI MOHCCN Data Science fellowship.
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