I am a first-year DPhil student in the StatML CDT program, working under the supervision of Prof. Robin Evans on diverse topics in causal inference. I completed my B.Sc. in Econometrics at Maastricht University and my M.Sc. in Statistics at ETH Zürich. Before starting my DPhil, I spent one year at Inria Montpellier (team PreMeDICaL) working with Julie Josse, Erwan Sconet & Jeffrey Näf on missing data methodology.

Causal inference, Missing data

I am a first year DPhil student, supervised by Tom Rainforth. I am also a part-time Research Scientist at the AI Security Institute (AISI) with the Science of Evaluations team.

Prior to the DPhil I was a Research Engineer at AISI, designing and conducting dangerous capability evaluations on frontier language models. I previously obtained an MSc in Artificial Intelligence from Imperial College London and an MMath from the University of St Andrews.

I am broadly interested in AI Safety and Security, and LLM evaluations. I'm currently interested in applying tools from Experimental Design and Active Testing to improve the efficiency and accuracy of LLM assessments.

I am a DPhil student on the StatML CDT. I completed my MMath in Mathematics and Statistics at Oxford (2025) where I developed an interest in modelling the spread of infectious diseases. My current work focuses on the use of machine learning on time series data to gain insight into the behaviour of broiler chickens. 

• Applications of statistics in public health monitoring and intervention
• Statistical machine learning

I am a first year DPhil StatML CDT student. Prior to this, I graduated from the University of Bristol with a M.Eng in Mathematics and Computer Science.

My interests span Bayesian statistics, statistical machine learning and the application of these fields within healthcare. I am quite keen in particular to explore how statistical and machine learning techniques can accelerate the field of healthcare. In parallel to this, I enjoy pondering upon questions related to the field, such as if AI is the future of medicine for instance!  

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Jessica is a PhD student in the Department of Statistics, where her work bridges the gap between advanced machine learning and extreme event prediction, particularly with regards to extreme climate events. Her background spans both technical ML and public policy, with prior experience applying computational methods to humanitarian decision-making, infrastructure investment, and global governance. She is particularly interested in the reliability of generative AI in high-stakes environments, specifically how synthetic weather data can be rigorously validated to ensure it accurately captures extreme tails of a distribution, including rare, extreme events that fall outside the historical record but are critical for long-term risk management.

Her research focuses on the intersection of generative modelling and extreme value theory, with an emphasis on developing formal validation frameworks for synthetic climate data. This involves combining statistical machine learning with spatio-temporal modelling to assess the trustworthiness of AI outputs for applications in energy systems, infrastructure planning, and environmental risk. Her work includes building benchmark datasets, designing controlled evaluation systems, and systematically comparing modelling approaches across different mechanisms of extreme event formation to provide a rigorous foundation for decision-making under uncertainty.