I am a third year DPhil student on the StatML CDT. The goal of my doctoral project is to model the humanitarian impact of disaster events such as earthquakes. I completed my undergraduate degree in Mathematics and Statistics at the University of Western Australia, and was awarded a Rhodes Scholarship in 2021. Outside of my studies, I enjoy swimming, ABBA/Taylor Swift club nights, and walks around University Parks with a good podcast.

My primary statistical interests are Bayesian statistics and MCMC methods. I am most driven by the applications of my work, and in the long run, would like to use statistics and/or machine learning to address societal or environmental challenges.

I am a mathematician who is currently pursuing a doctoral degree at the Mathematical Institute and the Department of Statistics of the University of Oxford. Within the Mathematical Institute, I am a member of the well-known Center for Doctoral Training in Industrially Focused Mathematical Modelling (InFoMM CDT). During my undergraduate degree, I mainly focussed on pure mathematics and its rigorous methodology. Since then, I have additionally become interested in the applied and data-driven side of the mathematical sciences. I have developed a particular focus on mathematical data science, programming, graphs and networks, deep learning, artificial intelligence and advanced statistical machine learning. In my current DPhil (= PhD) project at the University of Oxford I am collaborating with the research company Lhasa Limited to investigate novel graph-based machine learning techniques and their applications in chemistry and computational drug discovery.

Molecular machine learning techniques have recently shown great promise for important computational drug discovery tasks such as molecular property prediction and activity cliff prediction. The success of such methods, however, crucially depends on the way in which molecular compounds are transformed into informative feature vectors that can be fed into a machine learning pipeline. This is referred to as the problem of molecular representation. In my DPhil project, I am investigating the potential of modern graph-based molecular representation techniques to outperform classical molecular representations such as structural fingerprints and physicochemical descriptor vectors. I am particularly interested in developing novel self-supervised learning strategies for graph neural networks operating on molecular graphs in order to identify and remove hidden performance barriers of state-of-the-art molecular representation methods. The gained insights can be used to design new tailored deep learning architectures for important computational drug discovery tasks such as molecular property prediction and activity cliff prediction.

I rejoined the Department of Statistics as Director of Studies in 2008, having previously been a University Lecturer in the Department from 1992 to 2006. I was an undergraduate at the University of Cambridge and a graduate student in the Statistical Laboratory there. My PhD thesis was on dynamic routing in queueing networks, and subsequently I worked on related problems in both queueing and loss networks.

As Director of Studies, I undertake a range of duties related to academic management, strategy, examining and admissions, and contribute to teaching.

My work lies at the intersection of learning theory and optimization, with a particular emphasis on the mirror descent algorithm and its variants. I have previously studied Mathematics and Computer Science at the University of Oxford.

Statistical Learning Theory, Optimization

I am a DPhil student in the Department of Statistics at the University of Oxford, supervised by David Steinsaltz and Maria Christodoulou. In general, my research focuses on plant population dynamics in stochastic environments by incorporating Gaussian Process models. In particular, I am also interested in applying advanced simulation methods, like Particle filters for static or time-series models, and Bayesian methods, like Approximate Bayesian computation methods, to population modelling methods. Before joining Oxford, I completed my BSc in Statistics at University College London, where I created an R package with Paul Northrop for the following students in the department to study Markov chains. I then moved to Oxford to study for an MSc in Statistical Science and stayed on for the DPhil.

• Integral projection model
• Approximate Bayesian computation methods
• Gaussian process models
• Sequential Monte Carlo

Background in Mathematics particularly Probability Theory and Analysis in infinite dimensions.

Deep Learning, Kernel methods, Wasserstein gradient flow, Generalised Bayesian inference and most mathematical aspects related to deep learning and uncertainty quantification.

I am a third year DPhil student working under the supervision of Robin Evans and Dino Sejdinovic.

My research focuses on applying causal inference to improve machine learning methodology. I have mostly focused on using these methods to improve the fairness and explainability of machine learning methods.