Date(s) - 10 Nov 2022
7:30 pm - 9:00 pm
Prof Min Chen, Professor of Scientific Visualisation, Engineering Dept., University of Oxford
Information loss has commonly been blamed as a cause of bad decisions. A close examination of the actuality (and the underlying mathematical descriptions) reveals that information loss is ubiquitous in data analysis, data visualization, statistical inference, machine learning, human decisions, and human communication. The ubiquity suggests some likely benefit of information loss. In this talk, the speaker will discuss the need to measure information loss systematically in data intelligence workflows (e.g., machine learning and pandemic modelling) in a way similar to many types of measurements, such as force and energy. Being able to measure information loss will allow us to optimise data intelligence workflows.
Min Chen developed his academic career in Wales between 1984 and 2011. He is currently Professor of Scientific Visualization at Oxford University and a fellow of Pembroke College. His research interests include many aspects of data science in general, and visualization and visual analytics in particular. He has co-authored over 200 publications, including his recent contributions in areas such as theory of visualization, visual analytics for machine learning, and perception and cognition in visualization. He has worked 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; editor-in-chief of Computer Graphics Forum; and co-director of Wales Research Institute of Visual Computing. He is a fellow of British Computer Society, European Computer Graphics Association, and Learned Society of Wales.Add to calendar: iCal