AI and Microscopy for Biomedical Discovery

Our group is building a research programme at the interface of artificial intelligence, microscopy and biology. We aim to turn complex images into quantitative knowledge and create new ways to investigate how cells organise, change and interact in health and disease. We develop open, accessible and reproducible tools that enable the broader community to analyse and interpret biological imaging data. Our interests centre on three connected research directions:

AI for image-driven biomedical research

We develop AI-powered methods and advanced analytical pipelines for biomedical imaging. These include tasks such as segmentation, classification or tracking. We work with foundation models, self-supervised and representation learning approaches that capture biologically meaningful variation and help reveal phenotypes that are difficult to define manually. Ultimately, we aim to leverage AI as a tool to support biological hypothesis generation and testing in a data-driven manner.

Cell dynamics

We want to understand how cell shape, intracellular architecture and behaviour change across space and time. By combining live-cell imaging with quantitative analysis, we aim to develop dynamic phenotypes that connect cellular organisation and motion to cell state and function. We are particularly interested in cell migration and in how cancer cells move through and adapt to complex 3D microenvironments.

Data-driven microscopy

We explore microscopy systems in which image acquisition and analysis inform one another. Our goal is to use real-time information from images to decide where, when and how to observe a biological process, helping capture transient events during cell migration while reducing unnecessary imaging and data generation.

Ongoing Projects