Speaker:
Rahul G. Krishnan. Assistant Professor [CS & LMP]. The University of Toronto.
Rahul G. Krishnan is an Assistant Professor of Computer Science and Medicine (Laboratory Medicine and Pathobiology). He is a member of the Vector Institute where he holds a CIFAR AI Chair. His research has developed new algorithms for probabilistic inference, and applied machine learning to problems in healthcare such as modeling disease progression and risk stratification. He received his MS from New York University and his PhD in computer science from MIT in 2020. His research has been published in top machine learning venues such as NeurIPS, ICML, AISTATS and AAAI.
Overview:
Through a variety of case studies in chronic disease such as diabetes, multiple- myeloma and liver disease, this talk will highlight recent advances in machine learning that enable the discovery of structure in clinical data and the development of software tools to forecast patient biomarkers and quantify the risk of adverse outcomes
Objectives:
At the end of this activity, participants will be able to:
- Identify problems in their work, and datasets which if collected would allow them to train machine learning models to predict clinical outcomes of interest ,
- Highlight the difference between supervised learning, and unsupervised learning