Siamak Ravanbakhsh

Before joining McGill/Mila as an assistant professor, I had a similar position at the University of British Columbia. Before that, I was a postdoctoral fellow at the Machine Learning Department and the Robotics Institute at Carnegie Mellon University. I received my Ph.D. from the University of Alberta. I’m broadly interested in the problems of representation learning and inference in AI. In the past, I have studied approximate inference in graphical models as a framework that brings structure to a probabilistic worldview. More recently, I have become interested in symmetry as an alternative inductive bias towards the same goal of sample-efficient representation learning.