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[areas of research interest]

[e.g. educational background, biographical details]

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I make Machine Learning, Causal Inference & Bayesian Optimisation work in the real world.

2017 BA Philosophy, Politics, Economics @ University of Exeter, UK

2019 PhD Computer Science (incomplete), graduated MSc Computer Science @ Syracuse University, USA

2022 Spotify Research UK (during PhD at UCL)

2024 PhD Foundational Artificial Intelligence (Google Deepmind Scholar, £125k) @ University College London, UK

Theoretical

• Causal Inference
• Partial Identification
• Active Learning
• Bayesian Optimisation

Applied

• Efficient Experimentation in Pharma, Semiconductors and Materials
• Experimentation for Price Impact Models
• Causal Inference for Common Disease 
• Causality for Molecule Discovery

Working between both the Department of Statistics and Mathematical Institute on projects contributing to the Research Excellence Framework submission.

Leading and supporting on projects to troubleshoot and improve PSS operations and workflows.

I am a Florence Nightingale Bicentenary Fellow in Computational Statistics and Machine Learning at the University of Oxford, jointly affiliated with the Department of Statistics and the Leverhulme Centre for Demographic Science (LCDS). I completed a PhD in Statistics at the University of Washington, advised by Adrian E. Raftery and Carlos Cinelli. Prior to UW, I studied math and physics at the University of Cambridge and Northwestern University. I am originally from Chicago, Illinois.

My research focuses on development of methods and applications of (primarily Bayesian) statistics to inform decision-making in the health and social sciences. I am interested in tackling problems in

• causal inference
• model selection and hypothesis testing
• nonparametric and high-dimensional statistics
• design and analysis of experiments
• modeling of complex data (e.g., hierarchical, spatiotemporal, mechanistic, and infectious disease models)

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[areas of research interest]