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
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.
Sep 1, 2025
New paper on bait selection for BioID experiments
Congratulations to Vesal whose paper on genetic algorithms for bait selection in BioID protein proximity profiling experiments has been published in Nature Communications.
May 1, 2025
Hassaan wins best poster at ICLR LMRL workshop
Congratulations to Hassaan for winning best poster at the Learning Meaningful Representations of Life workshop at ICLR 2025 for his work on disentangled representation learning of spatial expression and pathology data.
Apr 29, 2025
Michael nominated for SITC WIC Young Investigator Symposium
Congratulations to Michael for being chosen as the Canadian Cancer Immunotherapy Consortium’s 2025 nominee for the WIC Young Investigator Symposium at SITC’s 2025 annual meeting!
Apr 25, 2025
Kieran at AACR annual meeting
Kieran will be giving a talk on at the AACR annual meeting 2025 on inferring tumour microenvironment ecosystems from single-cell atlases.
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