I’m a Ph.D. student at the University of Toronto studying machine learning and artificial intelligence under the supervision of Richard Zemel. While here, I have also had the privilege to work with Geoffrey Hinton, Brendan Frey and Ruslan Salakhutdinov. Previously, I completed a masters degree at the University of British Columbia under the supervision of Nando de Freitas (now at Oxford). Here’s my CV.
I try to maintain a broad set of interests within machine learning, although I specialize in neural networks. Most recently, I have become interested in Bayesian optimization for automatically tuning the meta-parameters of neural networks (and other machine learning models). This approach has already had tremendous success on several benchmark datasets and often out-performs experts in tuning these models. I think this is a promising avenue for making deep learning more accessible, both within the machine learning community and beyond.
In the past I’ve worked on graphical models, inference algorithms, Bayesian methods, time-series problems and unsupervised feature learning. My main focus has been applying a probabilistic perspective to machine learning methods. This approach often provides a principled way to analyze existing techniques while serving as a launching point for interesting new extensions.
Some applications I’ve worked on in the past include: object recognition, speech recognition, environmental monitoring, and ranking for web search.
Input Warping for Bayesian Optimization of Non-stationary Functions
Jasper Snoek, Kevin Swersky, Richard Zemel and Ryan Adams
International Conference on Machine Learning (ICML 2014)
[pdf, arXiv preprint]