My research interests are in the algorithmic aspets of learning. I am particularly interested in the intersection between variational inference, Monte Carlo methods, and optimization. My goal is to develop algorithms for learning complex models that are fast and practical, but also theoretically sound. Recently, I have been working on line-search methods for stochastic gradient descent.

I am also interested in Bayesian nonparametric models. Gaussian processes were my first introduction to Bayesian inference and I still find them fascinating.


I received my bachelor’s degree in computer science from UBC in 2018. During my batchelor’s, I worked with David Poole and Giuseppe Carenini on preference elicitation and was the primary architect and developer of Web ValueCharts, a visualization system for multi-criteria decision making. All the code is open source and available on github.

The last six months of my undergraduate degree were spent with Emtiyaz Khan at the RIKEN Center for Advanced Intelligence Project (AIP), where I worked on low-rank approaches to Gaussian variational inference in Bayesian neural networks.

Aaron Mishkin