I completed my Integrated Master's (MChem) in Medicinal and Biological Chemistry at the University of Edinburgh. In my final year, I worked at AstraZeneca in oncology computational chemistry, contributing to small molecule drug discovery projects. There, I gained experience developing and applying AI methods to support drug discovery efforts.

My research focuses on developing artificial intelligence methods for small molecule drug discovery. The ultimate aim is to create tools that accelerate the discovery and development of novel medicines, addressing unmet medical needs more efficiently, cost-effectively, and with improved outcomes.

Currently, my research is centered around the application of large language models (such as ChatGPT, Claude, Gemini, etc.) in chemistry. I investigate whether these models truly "understand" chemistry and their potential to significantly aid scientific discovery. In particular, I am exploring how these models can leverage chemical theory to generate new hypotheses, design experiments, and interpret experimental results.

My first preprint as part of the Oxford Protein Informatics Group (OPIG) examines the ability of the latest large language models (known as reasoning models) to interpret molecular structures. Our findings demonstrate that these models can successfully perform highly complex chemistry tasks.

Selected publications:

• Assessing the Chemical Intelligence of Large Language Models (preprint available on arXiv: https://arxiv.org/abs/2505.07735)
• SILVR: Guided Diffusion for Molecule Generation (Journal of Chemical Information and Modeling, https://pubs.acs.org/doi/full/10.1021/acs.jcim.3c00667)

I am a postdoctoral researcher working with Dr. Duncan Palmer, continuing work from my DPhil involving the study of individuals with genetically-unexpected phenotypes in the UK Biobank. I was a DPhil student in the Genomic Medicine and Statistics programme run by the Wellcome Centre for Human Genetics, with my thesis co-supervised by Prof. Cecilia Lindgren and Prof. Simon Myers. Prior to Oxford, I graduated from Brown University with a degree in applied mathematics and worked for two years in the Neale Lab at the Broad Institute of MIT and Harvard.

Complex disease, polygenic scores, obesity, outliers, biomarkers, genome/exome-wide association studies, rare variant burden

I am a first-year DPhil student in the Department of Statistics and the Pandemic Science Institute at the University of Oxford. I completed my MSc in Modelling for Global Health (2023/2024) at Oxford and hold a BSc in Economics and Statistics (2020-2023) from University College London, where I developed a strong interest in statistical modeling and public health. My current research focuses on using mathematical tools to better understand mosquito-borne diseases and to inform more effective vector control strategies. Additionally, I am involved in a project that applies statistical analysis and software development to interpret serological data. My work integrates advanced statistical methods, mathematical modeling, and computational techniques to better understand disease transmission and contribute to public health strategies.

• Mosquito-borne diseases
• Mathematical biology
• Serological data analysis
• Statistical inference and Software development for infectious disease modelling
• Public health applications, e.g., vaccine immunology

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I am a third-year PhD student with broad interests in probability theory, branching processes, random structures, and models from statistical physics. My current research focuses on the limiting behaviour of certain branching Brownian motion particle systems. Before starting my DPhil, I completed graduate studies at École Polytechnique and the University of Cambridge.

• Branching processes
• Brownian motion and random walks
• Statistical physics

Although my research is primarily theoretical, probabilistic reasoning arises naturally in many problems. I especially enjoy discovering simple and visual arguments. I am always open to collaboration-please feel free to reach out!

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I am a DPhil student in Statistics supervised by Prof. Pier Palamara. My primary interest is in complex trait analysis and association studies in large genomic datasets. Previously, I completed my Bachelor’s degree in Statistics and Computer Science at McGill University and an MSc in Statistical Science at the University of Oxford.

Statistical and population genetics; complex trait analysis; scalable methods for association studies in large genomic datasets.

I am currently a first-year DPhil student under the supervision of Prof. Geoff Nicholas, focusing on rankings and Bayesian inference. Prior to this, I spent five years at Alibaba as the Lead AI Data Scientist, where I led key initiatives involving large-scale AI and data platforms. I am also a qualified actuary, with two years of experience in statistical pricing at Sydney before transitioning into the tech sector.

• Partial Orders
• Statistical inference

Jesse is a first-year DPhil in Statistics student and recipient of the Clarendon Fund Scholarship. He works together with professors Frank Windmeijer, Ruixun Zhang, and Mihai Cucuringu.

Jesse's research encompasses the intersection of statistics, econometrics, and machine learning. In particular, Jesse has a strong interest in causality, the intricacies of machine and deep learning methodologies, and their interplay in understanding complex systems.

Before starting his DPhil, Jesse graduated with distinction and cum laude from the London School of Economics and Political Science and the University of Amsterdam, focusing on econometrical and machine-learning methodologies.