Min Chen, University of Oxford
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.
Visualization is where Information Theory Meets Psychology
Building a theoretical foundation for visualization and visual analytics is a collective responsibility of the community of visualization and visual analytics (VIS). There are many pathways for making contributions to this endeavour, including through the observation, development, and evaluation of practical VIS applications. In this talk, the speaker will focus on one particular pathway that connects VIS with information theory and psychology. One can anticipate such connections easily since all VIS processes deal with information while involving human perception and cognition. In most applications of information theory, such as communication, compression, and encryption, encoders and decoders are developed as pairs of machine-centric solutions. VIS offers an intriguing platform for studying phenomena and developing applications that feature machine-centric encoders and human-centric decoders, providing opportunities for advancing information theory. Meanwhile, any improvement of our fundamental understanding of visualization processes and visual analytics workflows — through information theory or any helpful theoretical development — will likely inform theoretical discourse in psychology. It is our ambition as well as obligation to find theories that can explain and measure phenomena in VIS, and predict the cost-benefit and guide the optimization of visual designs and visual analytics workflows. Hopefully such VIS theories will inspire further advancement in other disciplines including information theory and psychology.
Yvonne Rogers, UCLIC, University College London
Yvonne Rogers is a Professor of Interaction Design, the director of UCLIC and a deputy head of the Computer Science department at University College London. Her research interests are in the areas of interaction design, human-computer interaction and ubiquitous computing. A central theme of her work is concerned with designing interactive technologies that augment humans. A current focus of her research is on human-data interaction and human-centered AI. Central to her work is a critical stance towards how visions, theories and frameworks shape the fields of HCI, cognitive science and Ubicomp. She has been instrumental in promulgating new theories (e.g., external cognition), alternative methodologies (e.g., in the wild studies) and far-reaching research agendas (e.g., “Being Human: HCI in 2020″). She has published over 250 articles, including two monographs “HCI Theory: Classical, Modern and Contemporary” and “Research in the Wild”. She is a fellow of the ACM, BCS and the ACM CHI Academy. She was also awarded a Microsoft’s 2016 Research Outstanding Collaborator Awards and a EPSRC dream fellowship concerned with rethinking the relationship between ageing, computing and creativity.
Slowing Down How We Think With Visualisations
Most visualisations and data science tools have been developed to speed up human cognition so that users can efficiently and rapidly draw conclusions from the emerging patterns and anomalies being shown from their datasets. A core UX technique is filtering, enabling the selection and switching on and off of various options, at the touch of a finger. However, the downside of this kind of ‘speed-dial’ interaction is it often results in fixed ways of inspecting and ‘seeing’ data, preventing users from developing different ways of querying and exploring data. How can we design the UX side of visualisations to encourage other kinds of thinking out of the box? An alternative approach we have been developing is to deliberately design the UX to slow down users’ thinking. In particular, we have been developing agents that can probe the user, make suggestions, and even contest their thinking at opportune times. While this approach may seem counter-intuitive, we suggest that for certain settings and tasks, it can encourage different lines of thinking; disrupting routinized problem-solving steps and facilitating more creativity. In so doing, our aim is to enable users to visualise more possibilities in their own minds when interacting with external visualisations. In my talk, I will describe our recent research into how to design the UX to support a slower way of thinking.