Professional Summary
Im a first-year PhD student at UCL and a member of the Fundamentals of Statistical Machine Learning group.
I am supervised by Dr A. Barp and my research focuses on interpretability in deep learning models with applications to healthcare. Prior to starting my PhD I had a brief stint in industry as a Data Scientist at CourtCorrect where I focused on Retrieval Augmented Generation, LLM finetuning and alignment. I completed a Masters (MEng) in Engineering Science at the University of Oxford alongside a Masters in Statistics (MSc) from UCL.
Education
PhD Statistical Science (Deep learning Focus)
University College London
MSc Statistics
University College London
MEng Engineering Science
University of Oxford
Interests
I am interested in generative probabilistic models, including Diffusion Models, Normalising Flows, and GANs. More recently, my work has focused on the interpretability of deep neural networks.
My focus is on moving beyond post-hoc explanations by exploring model-centric approaches. This includes designing inherently interpretable architectures, like Concept Bottleneck Models (CBMs), as well as delving into mechanistic interpretability to understand the specific algorithms that networks learn.