I am a graduate researcher at the Vector Institute for Artificial Intelligence
, affiliated with the Machine Learning group
at the University of Toronto.
I work with Prof. David Duvenaud
on algorithms that
improve the speed and model flexibility for both deep learning and Bayesian machine learning.
More broadly, I am interested in machine learning algorithms that automatically discover underlying patterns in data and use them to generate new structured content.
I completed my BSc (2016) at the University of British Columbia majoring in both Statistics and Computer Science.
I spent summer 2016 working in Prof. Mark Schmidt
Machine Learning Lab where I developed unsupervised learning algorithms for a Matlab machine learning toolbox
I spent fall 2017 working with Ferenc Huszár
improving black-box optimization methods for general non-differentiable functions.
This term, I'm part of the teaching staff for CSC412: Probabilistic Learning
This summer, I will be working with
Microsoft Research Cambridge
on improving machine learning algorithms to solve problems in synthetic biology.