Professor Min Chen’s Recent Publication Makes Important Contribution To Visualization Literature

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In the year that commemorates the centenary birthday of Claude Shannon (1916-2001), the founding father of information theory, Pembroke Fellow and Professor of Scientific Visualisation, Professor Min Chen, and his co-authors published Information Theory Tools for Visualization, the first book that brings together all major uses of information theory in visualization literature.

Information theory is “the science of quantification, coding and communication of information” [Usher, 1984]. Visualization is about visually coding and communicating information. Not only can Information theory be used to optimise the design and delivery of visualization, but it also offers a mathematical confirmation that visualization is beneficial. The mathematical proof was discovered in 2015 by Prof. Chen and Professor Amos Golan (who was recently elected to a Pembroke Senior Associateship).

In addition to the book, 2016 has seen a series of theoretical advances in the field of visualization. In April, the Master of Pembroke College, Dame Lynne Brindley, opened the ‘Alan Turing Institute (ATI) Symposium on Theoretical Foundation of Visual Analytics’ in the British Library.

This symposium led to several events at the ‘IEEE VIS 2016’  forum in October 2016 in Baltimore, MD, USA, including a workshop on Principles and Guidelines in Visualization, and a panel on Pathways for Theoretical Advances in Visualization. The panel, which was chaired by Prof. Chen, received the best panel award.

Prof. Chen has also co-authored an information-theoretic paper titled ‘An Analysis of Machine- and Human-Analytics in Classification’, which describes how to estimate quantitatively soft knowledge in a data intelligence process. The paper was presented at IEEE VIS2016, and received the best VAST (Visual Analytics Science and Technology) paper award.

Professor Min Chen is an internationally established scientist in the field of visualization, with significant contributions in volume graphics, video visualization and theory of visualization. His current research interests include: visualizing “time” without using “time”, theory of visualization and causality discovery technology.