Dr. Kieran Campbell

PhD
Investigator

Lunenfeld-Tanenbaum Research Institute

We develop and apply state-of-the-art machine learning algorithms for single-cell, spatial and imaging datasets. We have specific interests in mapping how tumour genetics and its immediate surrounding environment in the body work together to shape its behaviour and influence how the disease progresses. To answer these questions, we use machine learning models to predict what happens when genes, cells or drugs are changed. This helps us identify new biomarkers and discover potential drug targets. We collaborate with a broad range of researchers both in Toronto and further afield, working both on technology development as well as focussing on specific areas including cancer (pancreatic, sarcoma, prostate), transplant biology and immunology. 

Telephone
Contact

Email: [email protected]

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Location

Room 875, 600 University Avenue
Toronto, M5G 1X5

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Accordion Items
  • 2020–present; Assistant Professor, Departments of Molecular Genetics, Statistical Sciences, Computer Science, University of Toronto, Toronto
  • 2020–present; Investigator, Lunenfeld-Tanenbaum Research Institute, Sinai Health, Toronto  
  • 2020–present; Faculty Affiliate, Vector Institute, Toronto
  • 2020–present; Associate Faculty, Ontario Institute for Cancer Research, Toronto
  • Banting postdoctoral fellowship at the Department of Statistics, University of British Columbia and Department of Molecular Oncology, BC Cancer Agency; 2017–2019
  • PhD Computational Genomics, University of Oxford, Oxford, England; 2014–2017
  • MPhil Computational Biology, University of Cambridge, Cambridge, England; 2013–2014
  • BSc Mathematical Physics, University of Edinburgh, Edinburgh, Scotland; 2009–2013 
  • 2025–2030 – Ontario Early Researcher Award
  • 2021–2031 – Canada Research Chair in Machine Learning in Translational Biomedicine

Notable publications

Computational design and evaluation of optimal bait sets for scalable proximity proteomics

Nature Communications, 2025

Segmentation aware probabilistic phenotyping of single-cell spatial protein expression data

Nature Communications, 2025

The impacts of active and self-supervised learning on efficient annotation of single-cell expression data

Nature Communications, 2024

Automated assignment of cell identity from single-cell multiplexed imaging and proteomic data

Cell Systems, 2021

Probabilistic cell-type assignment of single-cell RNA-seq for tumor microenvironment profiling

Nature Methods, 2019

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