The Campbell Lab works on a diverse set of problems at the intersection of machine learning and biomedical research. 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 work on many things including:
- Machine learning models for spatial ‘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
- Software development for the design and analysis of highly multiplexed imaging datasets
Latest news
Jul 2, 2026
Morris presents at ISBA in Japan
Morris is presenting a poster titled “Estimating Hierarchical Tissue Organization via Spatial Point Processes and Latent Factor Models” at ISBA 2026 in Nagoya, Japan.
Jul 2, 2026
Michael presents at UK PDAC meeting
Michael is presenting a poster on his work around sex-specific impact of HLA-LOH on tumour identity at the inaugural CRUK pancreatic cancer conference.
Jul 1, 2026
Kieran becomes Associate Professor
Kieran becomes an Associate Professor at the University of Toronto.
Apr 8, 2026
Kieran receives CSMB New Investigator Award
Kieran has been named the 2026 receipient of the New Investigator Award from the Canadian Society for Molecular Biosciences.
Jan 2, 2026
Welcome new graduate students
Welcome to Riya Alluri, Michelle Tong (co-advised by Alissa Greenwald), and Yufei Zhang (co-advised by Hartland Jackson) who join the lab as graduate students.
Jan 1, 2026
New paper on deep learning for spatial proteomics
Congratulations to Shanza whose paper benchmarking unsupervised deep learning methods to learn latent spaces associated with clinical outcomes from spatial proteomics data has been published in Bioinformatics Advances.
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