I studied at Trinity College Dublin and the University of Edinburgh, and did my PhD on the Oxford-Warwick Statistics Programme. I was then a postdoc at the University of Oxford, and then a Lecturer in Statistics at the University of Glasgow.

My research lies at the intersection of probability, statistics and computation, applied to problems in genetics. I am interested in what we can learn about evolution by analysing sequenced genomes: for instance, through reconstructing the shared genetic history of a sample of individuals, we can gain insights into past demography, understand how genetic variation arises and how it is shaped by natural selection to produce the patterns we observe in the data. 

I am a postdoc working with Professor Patrick Rebeschini. My research focuses on machine learning theory and statistical learning, with particular emphasis on heterogeneous data settings.

I have recently been working on two main problems: (i) designing and analysing algorithms for online learning where data arrives sequentially and evolves over time; (ii) I study the training dynamics of popular machine learning architectures such as neural networks and attention mechanisms, particularly how they behave under challenging conditions like non-stationarity, contamination, and distribution shifts. I am especially interested in understanding phenomena such as benign overfitting, early stopping, and in-context learning with data heterogeneity.

I completed my PhD at Cambridge in 2024, where I was advised by Professor Ramji Venkataramanan. My PhD work centered on information theory and statistical learning, motivated by fundamental questions such as: Given a complex statistical estimation problem, what is the minimal amount of data we need to estimate the underlying signal? Can we design polynomial-time, mathematically-principled algorithms that approach the minimum?

• Data heterogeneity: non-stationarity, contamination, and distribution shifts
• General first-order methods, e.g., gradient descent, approximate message passing
• Online learning and bandits
• Information theory and communication systems