I am a PhD student at Princeton
working on statistical machine learning
(as part of the Artificial Intelligence and Machine Learning
advised by Ryan Adams
in the Laboratory for Intelligent Probabilistic Systems
In 2018, I completed my MSc with David Duvenaud
Vector Institute for Artificial Intelligence
, while a student in Machine Learning group
at the University of Toronto.
I completed my BSc (2016) at the University of British Columbia in Statistics and Computer Science.
I spent summer 2016 working in 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.
During summer 2018, while an intern at Microsoft Research Cambridge
I collaborated on a novel class of deep generative models
for understanding and programming
information processing in biological systems.
As of summer 2019, I am an intern at Google Brain
in San Francisco, working with Durk Kingma
on a better understanding of representation learning in deep generative models.
More broadly, I am motivated in my research to push forward a theoretical understanding of deep learning,
in support of improving robustness and reliability of deep statistical models,
while exploring how new affordances in deep generative models can improve existing practices in scientific discovery and engineering design.