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.
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).
- Prof. Ghassemi was recently awarded a Canada Research Chair in Machine Learning for Health.
- Congratulations to Bret Nestor and Guanxiong Liu for their paper acceptances to MLHC 2019.
- Prof. Ghassemi will be teaching CS 2541 (https://cs2541-ml4h2019.
github.io) and C4M (https://c4m-uoft.github.io/), as well as hosting the Machine Learning for Health Unconference (http://www.ml4h.org) in Spring 2019.
- Prof. Ghassemi was appointed a Canada CIFAR AI Chair.
- Prof. Ghassemi was one of MIT Tech Review’s 35 Innovators Under 35.
- The ML4H group received a NSERC 2018 Discovery Grant.
- Prof. Ghassemi was a finalist for the AMIA 2018 Doctoral Dissertation Award and MIT’s 2018 Seth J. Teller Award for Excellence, Inclusion and Diversity.
- Invited panel on "Fairness and Bias in Precision Medicine" at AMIA 2019 Policy Forum, National Press Club in Washington, DC, December 5th, 2019.
- Ketnote 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.