Background
I am a third-year PhD student at the University of Cambridge supervised by Prof Lawrence. I am generally interested
in
practical applications of (probabilistic) decision-making frameworks.
I have spent the last few years working on various problems related to decision-making such as reinforcement
learning
[1],
Bayesian optimization, stochastic control/diffusion [2]
(diffusion) and experimental
design [3].
My research interests lie at the intersection of theory and practice in machine learning. Considering practical
problems
is essential in identifying critical themes to be addressed from a more theoretical perspective. This has led me to
work
on a variety of applications such as ice-sheet modelling [4],
electrical grid management [5] and robotics
[6]. You can find a lecture on my most recent work
here.
Before my PhD, I was a researcher in the MILA institute under the supervision of Prof Pineau and working towards a
Master’s Degree in Computer Science at McGill University. My research focused on Reinforcement Learning and Deep
Learning. I also interned at IBM in the Machine Learning for Healthcare group under the supervision of Dr El-Hay
where
we developed new techniques for causal inference [7] . My first
degree was in Mathematics
and
Computer Science at McGill University.