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[areas of research interest]

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

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[areas of research interest]

[e.g. educational background, biographical details]

[areas of research interest]

Before coming to Oxford, I completed a BSc in mathematics where I focused on probability and stochastic processes. I did my MSc in mathematical sciences here at Oxford, where I studied stochastic analysis and machine learning, and wrote my dissertation on the theory of diffusion models.

My research interests include causal inference, domain adaptation, and transfer learning theory, with a focus on developing theoretical understanding. More broadly, I’m interested in foundational questions in statistics and machine learning that draw on areas such as high-dimensional probability, stochastic processes, and graphical models, and that benefit from rigorous mathematical analysis. I am also interested in related topics such as robustness and the theoretical aspects of data augmentation and data selection.