ML4H

The Machine Learning for Health group (ML4H@cs.toronto) targets "Healthy ML", focusing on creating applying machine learning to understand and improve health.

We believe that health is important, and improvements in health improve lives. However, we still don't fundamentally understand what it means to be healthy, and the same patient may receive different treatments across different hospitals or clinicians as new evidence is discovered, or individual illness is interpreted. Health is unlike many success stories in machine learning so far - games like Go and self-driving cars - because we do not have well-defined goals that can be used to learn rules. The nuance of health also requires that we keep machine learning models "healthy" - working to ensure that they do not learn biased rules or detrimental recommendations.

Improving health requires targeting and evidence – our group tackles part of this puzzle with machine learning. There are many novel technical opportunities for machine learning in health challenges, and important progress to be made with careful application to domain.

Read more about our Research Directions, Publications, and


ML4H Hiring a Postdoc and Research Assistant for ***Fall 2019***

Our lab is seeking motivated postdoctoral researcher and research assistant. We have offices in the department of Computer Science at the University of Toronto, and at the Vector Institute.

More information can be found in Opportunities in ML4H (Joining/Volunteering).


Recent News


Upcoming Talks

    • Panel Discussion on The Need for Interpretable and Fair Algorithms in Health Care and Policy at JSM 2020, August 1-6, 2020.
    • Invited Talk at the Stanford Center for Bioethics Lecture Series, June 2, 2020.
    • Keynote Panel on Impact of AI for Life Sciences Career Expo 2020, May 14, 2020.
    • Invited Talk at Symposium on Artificial Intelligence for Learning Health Systems (SAIL), April 28, 2020.
    • General Chair for the ACM Conference on Health, Inference, and Learning (CHIL), April 3-5, 2020.
    • Invited Talk at the Stanford AI for Social Good Lecture Series, Feb 10, 2020.  
    • Co-Chair Duke Clinical Research Institute (DCRI) Think Tank on ML and clinical research, January 29-30, 2020, Washington DC. 
    • NeurIPS Workshop Co-Chair at NeurIPS 2019, Dec 9-14, 2019, Vancouver, BC.

Recent Talks

    • Invited Talk at the Global Forum for AI and Humanity at the Global Partnership on AI in Paris, France, October 29, 2019.
    • Keynote Talk at Machine Learning and the Market for Intelligence conference, The Rotman School, October 24, 2019.
    • Panelist at "Diversity & Inclusion in a Data-Driven World", 11th annual Connected Health Conference, Boston, MA, Oct. 16-18, 2019.
    • Fair Health and ML Summit, Data and Society Institute, New York City, Oct 11, 2019.
    • Toronto Health Data Hackathon Host, Centre for Social Innovation, October 4 - 5, 2019.
    • Invited Talk at TEDx UofT-Fields Salon, Oct 3, 2019.
    • Invited Talk at Ethics of AI in Context Series Talk @ University of Toronto Centre for Ethics, Oct 1, 2019.
    • Invited Talk at "Machine Intelligence in Healthcare: Perspectives on Trustworthiness, Explainability, Usability and Transparency" @ NIH/NCATS Workshop, July 12, 2019.
    • Keynote at iBest Sympsoium, June 14, 2019.
    • Invited Talk at "Wrong at the Root: Racial Bias and the Tension Between Numbers and Words in Non-Internet Data" Summer Cluster on Fairness @ Simons Institute, University of Berkeley, June 5, 2019.
    • Invited talk at Rotman Centre for Health Sector Strategy Conference, May 24, 2019.
    • Invited talk at Stanford Big Data in Precision Health conference, May 22, 2019.
    • Participant at CIFAR AI for Health (AI4H) Roundtable, led by Dr. Elissa Strome (CIFAR) and Dr. Tim Evans (World Bank), May 13, 2019.
    • Panelist on Raw Talk Live Event @ JLabs Toronto, May 7, 2019.
    • Keynote speaker at MIT Club of Toronto AI/ML Talks @ Vector Institute, April 16, 2019.
    • Inaugural invited talk at Microsoft Research Montreal AI Distinguished Lecture Series @ MILA, March 25, 2019.
    • Keynote at Women in Data Science (WiDS) Conference @ Stanford, March 4, 2019.