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Harry Strange
University College London
London, United Kingdom
 

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.


     

Deep medicine: how artificial intelligence can make healthcare human again
Topol E., Basic Books, Inc., New York, NY, 2019. 400 pp.  Type: Book (978-1-541644-63-2)

As a result of the explosive growth of artificial intelligence (AI) in recent years, it is predicted that up to 47 percent of jobs may be automated away in the future [1]. Such disruption is often focused on blue-collar professions. Ho...

 

Taming uncertainty
Hertwig R., Pleskac T., Pachur T., Center for Adaptive Rationality ., The MIT Press, Cambridge, MA, 2019. 488 pp.  Type: Book (978-0-262039-87-1), Reviews: (1 of 2)

Dealing with uncertainty lies at the heart of what it means to be human. Every day we use limited information to make decisions on the basis of predicted future events, which are themselves shrouded in uncertainty. While many of the de...

 

Search and optimization by metaheuristics: techniques and algorithms inspired by nature
Du K., Swamy M., Birkhäuser Basel, New York, NY, 2016. 434 pp.  Type: Book (978-3-319411-91-0)

Search and optimization algorithms form the backbone of most modern artificial intelligence (AI) techniques. Many of the algorithms that follow the traditional path of AI use search to find optimal (or near optimal) solutions to a give...

 

 Twenty lectures on algorithmic game theory
Roughgarden T., Cambridge University Press, New York, NY, 2016. 352 pp.  Type: Book (978-1-316624-79-1)

I often find myself deliberately avoiding books that are based on university courses. In my experience, such books tend to feel rushed and thrown together rather than carefully planned out. So it was with some trepidation that I sat do...

 

 Introduction to statistical machine learning
Sugiyama M., Morgan Kaufmann Publishers Inc., San Francisco, CA, 2016. 534 pp.  Type: Book, Reviews: (1 of 2)

Recently, I found myself giving an impromptu book review to someone in the bookshop near to my office. I noticed that a man was browsing through a book on machine learning that I had purchased a few months ago. I won’t mentio...

 
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