I am a first year PhD student in the probability group supervised by Alison Etheridge and Maria Bruna. My current research focuses on characterising phase transitions of interacting particle systems using tools from information geometry.

The particle systems I work with arise from mathematical models of population genetics and active matter. 

I completed my undergraduate studies in mathematics and economics at École Polytechnique. After that, I did Part III of the mathematical tripos at Cambridge and the second year of the Probability and Random models masters in Paris (Sorbonne / ENS). My undergraduate thesis was on topological data analysis, and my master's thesis was on information quantities of interacting particle systems. I also wrote a Part III essay on critical exponents for planar percolation.

My work is supported by the Clarendon Scholarship and the Keble Sloane Robinson Scholarship.

Teaching:

• Probability on graphs and lattices (MT25): TA
• Applied probability (HT26): TA & Tutor

• Probabilistic modeling in population genetics
• Active matter
• Information theory
• Interacting particle systems and their scaling limits

Besides the projects that I am working on for my PhD, I have a long-standing interest in econometrics and probabilistic methods in machine learning.

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I am a second-year DPhil student in the CDT in AI for the Environment, working on mathematical models for disease surveillance and pandemic preparedness. My research focuses on early epidemic dynamics, network epidemiology, and surveillance strategies for pathogen detection. I am also interested in how climate change affects the spread of vector-borne diseases, with the goal of improving intervention strategies under changing environmental conditions.

After completing my BSc in Mathematics at Warwick University (2023), I earned my MSc in Mathematical Modelling and Scientific Computing from Oxford (2024), where my dissertation explored methods for understanding uncertainty and inference in network epidemic models.

• Disease surveillance and early warning systems
• Network epidemiology and metapopulation dynamics
• Climate-sensitive vector-borne disease dynamics
• Parameter inference and uncertainty quantification

HBSc in Bioinformatics and Computational Biology from the University of Toronto (2025) 

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Julia is a DPhil Statistics student, supervised by Frank Windmeijer, and is the inaugural recipient of the Oxford-Nuffield-Sir David Cox Graduate Scholarship. Her research focuses on hybridizing econometrics, statistics, and machine learning tools to gain deeper insights and build novel models, and she is particularly interested in understanding the implications of her work and results from a policy perspective. 

Prior to Oxford, Julia graduated Summa Cum Laude with Highest Honors in Economics from Harvard, with a secondary in Mathematical Sciences.

• Causal Inference
• Networks
• Machine learning

I am a first-year DPhil student in Statistics exploring how machine learning can enhance complex decision-making in healthcare delivery. Supported by the Ellison Graduate Scholarship, my research focuses on clinical decision support systems for chronic disease, with a particular interest in neurovascular care. I previously earned my MS in Management Science & Engineering and BA/BS degrees in Economics and Engineering from Stanford University.

• Machine learning
• Foundation models
• Clinical decision support systems
• Personalized medicine
• Neurovascular care