I'm a first year DPhil student interested applying geometric deep learning methods to binding affinity prediction. Specifically, I'm interested in how to best represent a molecule interacting with a protein and developing methods to improve the generalisability of binding affinity models.

Small molecule drug discovery, and machine learning.

I'm a first-year CDT student looking to work on machine-learning techniques for airline revenue management.

MCMC methods, reinforcement learning

I'm a first year DPhil student, co-supervised by Yee Whye Teh and Tom Rainforth. Before this I completed an integrated masters in Information and Computer Engineering at Cambridge, with my final year project on improving neural transducer-based speech recognition system algorithms.

I am currently working on improving understanding of when and why training fails in reinforcement learning. Broadly I'm interested in probabilistic machine learning, meta reinforcement learning, robust causal representation learning, and in general understanding what models are learning, how they learn, and why.

I am a doctoral student in the Modern Statistics and Statistical Machine Learning CDT, supervised by Chris Holmes, Habib Ganjgahi and Thomas Nichols. I work on Statistical machine learning and image analysis, with emphasis on applications to neuro-imaging data. I previously studied Mathematics at Imperial College London.

• Variational Bayesian methods
• Statistical machine learning
• High-dimensional statistics

I am a Second Year DPhil student on the StatML programme, supervised by Patrick Rebeschini and George Deligiannidis. My current work focuses on computational optimal transport. Prior to my graduate studies, I obtained an MMath degree from the University of Oxford.

Computational Optimal Transport, Learning Theory, Optimization