I'm a PhD student in Computer Science at the University of Toronto. I'm interested in reinforcement learning, deep learning, and Bayesian learning. I'm co-supervised by David Duvenaud and Quaid Morris.
Reinterpreting Importance-Weighted Autoencoders
The standard interpretation of importance-weighted autoencoders is that they maximize a tighter lower bound on the marginal likelihood. We give an alternate interpretation of this procedure: that it optimizes the standard variational lower bound, but using a more complex distribution. We formally derive this result, and visualize the implicit importance-weighted approximate posterior.
MSc Research Paper
Personalized cancer strategies are currently being hindered by intratumor heterogeneity.
One source of heterogeneity, clonal evolution, can lead to genetically distinct subpopulations
within a sample. Through the use of subclonal reconstruction methods, we can obtain estimates
of the subpopulation proportions within a single sample. Here, I leverage these proportion
estimates by incorporating it into the deconvolution of tumour gene expression data in order to
estimate the subclone specific gene expression profiles.
As a side project, I started a deep learning company that helps businesses automate their workflow and learn from their data. We are currently serving a driving safety company where we automate the classification of their driving videos for their driver safety review process.
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