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.