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
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.
Jan 4, 2025
New lab publication
Congratulations to Jett and others in the lab for their new paper in Nature Communications introducing evaluation metrics, a gold-standard dataset, and a new probabilistic machine learning method to help infer single-cell expression profiles from multiplexed imaging datasets in the presence of segmentation errors.
Nov 30, 2024
New preprint
Congratulations to Matt and the rest of the lab on their new preprint using scRNA-seq and a novel antibody screening strategy to design and validate antibody panels.
Oct 6, 2024
New preprint
Congratulations to Vesal on his new preprint on how to design and evaluate protein bait subsets for BioID profiling.
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