PhD Researcher in Computational Pathology and Multimodal Machine Learning

I develop interpretable and generative machine learning methods for jointly analysing whole-slide histopathology images and molecular omics data.

My PhD research aims to uncover clinically and biologically meaningful cross-modal associations in cancer, with a particular focus on treatment response and survival.

• Multimodal learning
• Computational pathology
• Histopathology–omics integration
• Generative modelling
• Interpretable machine learning
• Cancer survival and treatment response

• TIAgent — Natural-language construction and inspection of computational pathology workflows.
• EcoAdvDep — Computer-vision and statistical analysis of zebrafish behaviour.
• Capstone Thesis — Improved Text-conditioned diffusion for synthetic histopathology image generation.

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