Prof. Dr. Min Chen
Professor Min Chen
BSc, PhD, FBCS, FEG, FLSW
Professor of Scientific Visualisation
Fellow of Pembroke College
University of Oxford
Min Chen developed his academic career in Wales between 1984 and 2011. He is currently the professor of scientific visualization at Oxford University and a fellow of Pembroke College. His research interests include visualization, computer graphics and human-computer interaction. He has co-authored some 190 publications, including his recent contributions in areas of theory of visualization, video visualization, visual analytics, and perception and cognition in visualization. He has been awarded over £11M research grants from EPSRC, JISC (AHRC), TSB (NERC), Royal Academy, Welsh Assembly Government, HEFCW, Industry, and several UK and US Government Agencies.
He is currently leading visualization activities at Oxford e-Research Centre, working on a broad spectrum of interdisciplinary research topics, ranging from the sciences to sports, and from digital humanities to cybersecurity. His services to the research community include papers co-chair of IEEE Visualization 2007 and 2008, Eurographics 2011, IEEE VAST 2014 and 2015; co-chair of Volume Graphics 1999 and 2006, EuroVis 2014; associate editor-in-chief of IEEE Transactions on Visualization and Computer Graphics; and co-director of Wales Research Institute of Visual Computing. He is currently the co-editor-in-chief of Computer Graphics Forum. He is a fellow of British Computer Society, European Computer Graphics Association, and Learned Society of Wales.
Title: Building a Theoretical Foundation for Visual Analytics
In this talk, the speaker will first report the discussions during the recent Alan Turing Institute event on Theoretical Foundation of Visual Analytics, and summarize the State of the Art in the four aspects of such a foundation, namely taxonomies, principles and guidelines, conceptual frameworks and models, and quantitative laws. The speaker will then describe an information-theoretic metric for measuring the cost-benefit of machine- and human-centric processes in data intelligence, and demonstrate that one can mathematically reason the merits of visualization and interaction in most data intelligence applications.