PeopleofSuperintelligence

Researcher

Benjamin Haibe-Kains

Benjamin Haibe-Kains is a bioinformatics researcher who applies machine learning to cancer data and treatment response. He earned a PhD in bioinformatics at the Université Libre de Bruxelles and completed a Fulbright-supported postdoctoral fellowship at Dana-Farber Cancer Institute and Harvard School of Public Health before joining Princess Margaret in 2013. His 2012 three-gene breast-cancer model and 2013 genomic drug-response predictors used computational methods to study cancer treatment; he also coauthored PharmacoDB and a 2020 paper on transparent AI for breast-cancer screening. He joined the Royal Society of Canada’s College of New Scholars in 2022 and renewed a Canada Research Chair in 2025. By 2026, he had led an immunotherapy-response study, helped develop the Seven Hallmarks of Predictive Oncology, and contributed to the PMATCH clinical-trial pilot; he is UHN’s Executive AI Scientific Director and Co-Director of UHN AI Hub.

Timeline

  1. By 2013

    Training before Princess Margaret

    Before joining Princess Margaret in 2013, he earns a PhD in bioinformatics at the Université Libre de Bruxelles and completes a Fulbright-supported postdoctoral fellowship at Dana-Farber Cancer Institute and Harvard School of Public Health.

  2. 2012

    A three-gene breast-cancer model

    In 2012, he develops a simple, robust three-gene model for breast-cancer molecular subtyping. The model is more robust than published classifiers and has similar prognostic value to more complex gene signatures.

  3. 2013

    Cancer drug-response predictors

    In 2013, he trains genomic predictors of drug response for 15 drugs using Cancer Genome Project data; half fail validation on Cancer Cell Line Encyclopedia data. His comparison of the studies finds that their drug-sensitivity measurements are highly inconsistent.

  4. 2017

    A cancer drug-screening database

    In 2017, he coauthors PharmacoDB, a database integrating large cancer pharmacogenomic studies. Curation of cell-line and chemical-compound identifiers increases the overlap between datasets and lets users compare studies to extract robust drug-response phenotypes.

  5. 2020

    Transparent and reproducible AI

    In 2020, he coauthors “Transparency and reproducibility in artificial intelligence.” The paper argues that missing method details and algorithm code weaken the scientific value of a breast-cancer screening study, and outlines ways to improve reproducibility.

  6. 2022–2025

    RSC membership and research chair

    In 2022, he joins the Royal Society of Canada’s College of New Scholars, Artists and Scientists as a University of Toronto faculty member in Medical Biophysics. In 2025, he renews a Tier 2 Canada Research Chair in Computational Pharmacogenomics and works as a Senior Scientist at Princess Margaret and professor of Medical Biophysics at U of T; his team uses machine learning and AI to predict drug response and resistance.

  7. By 2026

    Immunotherapy, standards and patient matching

    By 2026, he leads a study with Université de Montréal collaborators that analyzes clinical and molecular data from 26 immunotherapy trials across 12 cancer types and three treatment classes, covering more than 3,600 patients; the study identifies a 100-gene signature for response prediction. With international collaborators, he develops the Seven Hallmarks of Predictive Oncology to assess the quality, reliability, and clinical potential of cancer AI models. In 2026, PMATCH operates as a Princess Margaret pilot that uses machine learning to match patients with clinical trials.

  8. As of October 2026

    AI leadership at UHN

    As of October 2026, he is UHN’s Executive AI Scientific Director and Co-Director of UHN AI Hub, Senior Scientist and Allan Slaight Scientist at Princess Margaret, and Scientific Director of the Cancer Digital Intelligence Program.

Career

  1. NowUniversity Health NetworkScientific Director
  2. Dana-Farber Cancer InstitutePostdoctoral fellow
  3. Harvard School of Public HealthPostdoctoral fellow
  4. University of TorontoProfessor

Education

  1. Free University of BrusselsPhD

From the biography.

  1. The median h-index is 48 at universities and 14 at frontier AI labsIncludes University of Toronto. The median h-index is 48 at universities and 14 at frontier AI labs. We compare publishing careers, recent citations, dual appointments and job moves across 2,420 university researchers and 308 frontier-lab researchers.
  2. The Massachusetts Institute of Technology trained or employed 44 of the 187 AI major-prize winners, more than any other universityIncludes University of Toronto. The Massachusetts Institute of Technology trained or employed 44 of the 187 AI major-prize winners, more than any other university. We compare doctorate and 5+ year employment links, career timing and current workplaces for 187 major-prize and 1,111 best-paper winners.
  3. 84% of people shaping AI with recorded undergraduate and doctoral schools changed institutions for their doctorateIncludes University of Toronto. 84% of people shaping AI with recorded undergraduate and doctoral schools changed institutions for their doctorate. Across 2,116 people with both an undergraduate degree and a doctorate recorded, we trace which undergraduate schools feed which doctoral programs, how often students stay at the same school, and how paths differ by birth-country region.
  4. More people shaping AI studied at Massachusetts Institute of Technology than at any other institutionIncludes University of Toronto. We estimate where the people shaping AI studied and where their careers led. Massachusetts Institute of Technology leads with 467 former students, including 291 recorded doctorates.

Sources and credits

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  1. Haibe-Kains | Structural Genomics Consortium thesgc.org. Checked 21 October 2026.
  2. Meet Dr. Benjamin Haibe-Kains @PMResearch | UHN Research uhnresearch.ca. Checked 21 October 2026.
  3. PharmacoDB: an integrative database for mining in vitro anticancer drug screening studies ncbi.nlm.nih.gov. Checked 21 October 2026.
  4. Transparency and reproducibility in artificial intelligence - Maastricht University cris.maastrichtuniversity.nl. Checked 21 October 2026.
  5. Nine U of T faculty members named to Royal Society of Canada’s College of New Scholars, Artists and Scientists utoronto.ca. Checked 21 October 2026.
  6. Canada’s research leaders at UHN uhn.ca. Checked 21 October 2026.
  7. Improving patient matching to therapy (PMATCH): streamlining clinical trial criteria to guide precision oncology genomecanada.ca. Checked 21 October 2026.