Machine learning for healthcare deployment: what works and what doesn’t (yet)
As part of our LMP Seminar Series we are delighted to welcome our speaker:
Dr. Anna Goldenberg, PhD
Research Co-Lead, TCARIEM
Varma Family Chair in Biomedical Informatics and Artificial Intelligence
CIFAR AI Chair
Professor
Department of Laboratory Medicine & Pathobiology
Department of Computer Science
University of Toronto
Talk title: "Machine learning for healthcare deployment: what works and what doesn’t (yet)"
Hosted by
How to join
The event will be in person, no need to register. Students and trainees must attend in person.
For faculty members who need to attend remotely, please register to receive the zoom link. Registration must be received by noon on Tuesday.
If you have any questions, please contact lmp.chairadmin@utoronto.ca for more details.
Wednesday, March 25, 2026
11 am - 12 pm
MSB 2170
Medical Sciences Building
University of Toronto
1 King’s College Circle
Toronto, ON M5S 1A8
Details are sent to the LMP community in the Friday events bulletin.
Speaker bio: Dr. Anna Goldenberg
Dr Anna Goldenberg is a professor in the departments of Computer Science and the Laboratory Medicine and Pathobiology at the University of Toronto. She is a Varma Family Chair in Biomedical Informatics and Artificial Intelligence at SickKids Research Institute as well as a CIFAR AI chair at the Vector Institute. She co-chairs the research arm of the Temerty Centre for AI at UofT and co-leads SickKids AI Services. Additionally, Dr Goldenberg is a director of an AI in Healthcare Master’s concentration under the Masters of Applied Computer Science umbrella joint between DCS and LMP. Dr Goldenberg trained in machine learning at Carnegie Mellon University, with a postdoctoral focus in computational biology and medicine. The current focus of her lab is on developing and deploying machine learning models to healthcare, with particular emphasis on time series modeling, multi-modal data integration, wearable data and early-warning clinical systems. Dr Goldenberg’s lab is strongly committed to creating responsible AI to benefit patients across a variety of conditions.
