Currently working as a research fellow at Aberystwyth University, Harry Strange’s main interests lie in the fields of medical imaging, computer vision, and machine learning. Of particular interest is the development and application of manifold learning algorithms—statistical methods that help to understand and analyze high-dimensional spaces. His recent book, Open Problems in Spectral Dimensionality Reduction (Springer), seeks to provide an overview and discussion of the area of manifold learning. It allows researchers who are new to the field to quickly get up to speed and understand the strengths and limitations of manifold learning. As well as this, Harry works closely with clinicians and other biomedical researchers looking at the application of computer vision to areas such as fibrinogen segmentation, wheat CT imaging, and topological analysis of medical images.

Harry’s current role focuses on engaging clinicians with academics by providing workshops and meetings that help build capacity for future collaborations and grants. Before taking up his post as a research fellow, Harry worked as a post-doctoral research assistant focusing on novel methods for segmenting and analyzing mammographic and histopathology images. This work led to the development of techniques that improved the segmentation accuracy of high-risk mammograms and also provided new ways of representing and classifying mammographic microcalcifications. Within the field of histopathology, Harry’s research has focused on developing new tools to help pathologists analyze muscle biopsy cases.

Outside of academia, Harry is an avid reader and enjoys nothing better than a single malt, an open fire, and a good biography. He is married to Joanna, and they currently live in a converted stable just outside of Aberystwyth.