Welcome!

Welcome to my personal website. I am a 3rd year PhD student working on Deep Reinforcement Learning and Search, with a focus on solving optimization and control problems. My research explores how to leverage compute efficiently during both training and testing to optimize performance.

Specifically, I have explored the use of probabilistic inference as a policy improvement operator through my work Sequential Monte Carlo Policy Optimisation, leverging inference during both training and test time. I have also explored meta-learning search processes, treating search itself as an optimization problem. I am currently working on learning latent spaces in Reinforcement Learning in both continuous and discrete settings. In you are interested in any of these topics don’t hesitate to reach out for a chat!

About me

I am currently a PhD candidate at the University of Amsterdam, supervised by Dr. Herke van Hoof within the AMlab group. I hold a Master’s in Artificial Intelligence from the University of St Andrews and a Bachelor’s degree in Mathematics with Economics from the London School of Economics.

I have gained practical experience through internships working with companies such as InstaDeep and the startup Techspert. My work has been accepted at top-tier conferences such as NeurIPS and ICML. I also have experience working in asset management, having started my career as a quant in the multi-asset team at Aberdeen Standard Investments.