Hi, I’m Phil. I work at Microsoft Research, Montreal as a researcher. I want to understand the ways in which actionable information can be distilled from raw data. I work with methods based on deep learning — i.e. using large, non-linear, parametric function approximators.
I completed my PhD at McGill University in early 2016, guided by my helpful advisor Doina Precup. I joined Maluuba as a researcher in January 2016, and worked there until we were acquired by Microsoft in early 2017.
Microsoft Research Podcast
Episode 101 | December 4, 2019 - Deep learning methodologies like supervised learning have been very successful in training machines to make predictions about the world. But because they’re so dependent upon large amounts of human-annotated data, they’ve been difficult to scale. Dr. Phil Bachman, a researcher at MSR Montreal, would like to change that, and he’s working to train machines to collect, sort and label their own data, so people don’t have to. On the podcast, Dr. Bachman gives us an overview of the machine learning landscape and tells us why it’s been so difficult to sort through noise and get to useful information. He also talks about his ongoing work on Deep InfoMax, a novel approach to self-supervised learning, and reveals what a conversation about ML classification problems has to do with Harrison Ford’s face.