Sir Henry Wellcome Research Fellow


I am a Sir Henry Wellcome Fellow at the Institute of Biomedical Engineering and Big Data Institute at the University of Oxford. I am interested in modelling multi-scale protein functions to understand how functions at the molecular level propagate to cellular, tissue and organ levels. The activity of proteins is highly dependent on cell context. Therefore, it is crucial to incorporate cell microenvironment and morphology through the use of multi-modal imaging techniques. This requires developing innovative quantitative imaging and analysis methods for modelling biological behaviour at cellular, tissue and organ levels as well as integration of heterogeneous datasets.

While at Oxford I pioneered the concept of knowledge-driven machine learning. The only way we can harness the power of big biomedical data is through the development of intelligent systems that systematically incorporate existing biological knowledge. Such systems can guide machine learning approaches to discover new patterns in such rich datasets. I already demonstrated that such an approach can facilitate the discovery of context-dependent gene functions when applied to large scale genetic screens. Compared to previous methods, knowledge-driven machine learning enabled the extraction of far more information from each dataset. I also developed multiple deep learning and image analysis algorithms for classifying microscopy imaging data and profiling biological phenotypes at subcellular, cellular and tissue levels.

I did my PhD at the Institute of Cancer Research in London with Prof Chris Bakal. While at the ICR I developed methods for integrating phenotypic data with gene expression, modelling of the relationship between cell signalling and its context, and modelling the dynamics of cell morphogenesis. In these studies, I discovered new links between cell shape and breast cancer progression.

I am also passionate about data visualisation and science communication. I devised PhenoPlot, one of the first tools that is specifically designed for visualising phenotypic data. This method facilitates the interpretation of high dimensional data by generating pictorial representations of cells based on hundreds to thousands of measurements.


  • Multiplexed Imaging
  • High Throughput Microscopy
  • System Genetics
  • Digital Pathology
  • Data Visualisation

Recent Publications


  • Old Road Campus Research Building, Oxford, OX3 7DQ
  • DM Me