Min Chen BSc PhD FBCS FEG FLSW

Min Chen developed his academic career in Wales between 1984 and 2011. He previously held research and faculty positions at Swansea University (i.e., research officer from 1987, lecturer from 1990, senior lecturer from 1998, and full professor from 2001). He is currently professor of scientific visualization at Oxford University and a fellow of Pembroke College.

His research interests include data visualization, data science, computer graphics, computer vision, and human-computer interaction. He has co-authored over 200 publications, including his recent contributions in areas such as theory of visualization, video visualization, visual analytics, VIS4ML, 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. During the COVID-19 pandemic, he coordinated the RAMPVIS project for supporting epidemiological modelling. He is a fellow of the British Computer Society, European Computer Graphics Association, and Learned Society of Wales.

Personal website

Min Chen's primary research interest is data science in general and data visualization in particular, and he considers Data Science as the scientific discipline that studies human and machine processes for transforming data to decisions and/or knowledge. Its main goal is to understand the inner workings of different data intelligence processes, such as statistical inference, algorithmic reasoning, human thinking, and collaborative decision making, and to provide a scientific foundation to underpin the design, engineering, and optimization of data intelligence workflows composed of human and machine processes.

Theoretical Data Science is a major branch of Data Science that focuses on the mathematical theories that underpin all aspects of data science and enable abstract modelling of data intelligence workflows.

Applied Data Science is a major branch of Data Science that focuses on the technologies (e.g., data mining, data visualization, machine learning, etc.) for supporting the design, engineering and optimization of data intelligence processes and workflows.

Min Chen has made technical contributions to the following research topics:

• Visual Analytics. For many data intelligence problems, there is no fully-automated solution, likely because of the complex information space, the inadequate sampling (e.g. sparse or dated samples), the complexity of the algorithm, and so on. Visual analytics represents a methodology for bringing machine-centric processes (e.g., statistics and algorithms) and human-centric processes (e.g., visualization and interaction) together and designing an optimised workflow for such data intelligence problems. In this respect, the scientific essence is to understand and measure the relative merits of machine and human processes, while the engineering essence is optimisation rather than brute-force automation.
• Theories, Metrics and Empirical Studies. In the scientific world, a theory is a fact-based framework for explaining a set of observed phenomena or events. It is typically formulated to facilitate falsifiable predictions about some causal relations. It often involves quantitative measures, enabling analytical inference and numerical simulation. At the moment, information theory, which underpins tele- and data communication, has shown to be able to explain numerous phenomena of perception, cognition, emotion, and interaction in visualization. The key to such an explanation is the counter-intuitive cost-benefit measure, where entropy reduction (or information loss) is viewed as a merit rather than a demerit. The cost-benefit measure has been successfully used to analyse the effectiveness of performing visualization tasks in different virtual environments, optimise the trade-off between human and machine processes in data intelligence workflows, quantify informative contributions of human knowledge to machine learning, and explain the role of human knowledge in visualization processes, especially when there is significant information loss (e.g., underground maps and volume visualization).
• Video Visualization. Video visualization is concerned with the creation of a new visual representation from an input video to reveal important features and events in the video. It typically extracts meaningful information from a video and conveys the extracted information to users in abstract or summary visual representations, which are typically more compact than the input video itself. Video visualization is not intended to provide fully automatic solutions to the problem of making decisions about the contents of a video. Instead, it aims at offering a tool to assist users in their intelligent reasoning while removing or reducing the burden of viewing videos. In particular, it can be used to fill in many gaps in practice where automated computer vision is yet to provide deployable solutions. This aim justifies deviation from the creation of realistic imagery, and allows simplifications and embellishments, to improve the understanding of the input video. The fundamental challenge is: Can we see time (i.e., temporal information) without using time (i.e., an animation)?
• Volume Graphics and Volume Visualization. Volume graphics is concerned with graphics scenes, where models are defined using volume representations instead of, or in addition to, traditional surface representations. It is a study of the input, storage, construction, manipulation, display, and animation of volume models in a true three-dimensional (3D) form. Its primary aim is to create realistic and artistic computer-generated imagery from graphics scenes comprising volume objects, and to facilitate the interaction with these objects in graphical virtual environments. Volume visualization is also concerned with volume data representations that are used to store measured physical attributes of real-world objects and phenomena, or to represent computer-generated models and their attributes in volumetric forms. Although it is typical and conventional for volume datasets (such as in computed tomography) to correspond spatially to the 3D physical world, it is also common in some visualization applications to use volume datasets to store non-spatial physical data as well as abstract information.

• Coordinator of the RAMPVIS project for supporting epidemiological modelling (2020-2022)
• Editor-in-chief of Computer Graphics Forum (Wiley and EG) (2016-2019)
• Papers co-chair of IEEE VAST 2014 and IEEE VAST 2015
• Conference co-chair of EuroVis 2014
• Papers co-chair of Eurographics 2011
• Papers co-chair of IEEE Visualization 2007 and 2008
• Associate editor-in-chief of IEEE Transactions on Visualization and Computer Graphics (2011-2014)
• Associate editor of Elsevier Computers & Graphics
• Co-director of Wales Research Institute of Visual Computing (2009-2011)
• Co-director of Centre for Communication and Software Technologies, one of 18 CETICs (2002-2009)
• Deputy head and acting head of Swansea Computer Science (2009/10, 2010/11)

Here is a small selection of Min Chen's publications.

Visual Analytics and Machine Learning

M. Chen and D. S. Ebert. An ontological framework for supporting the design and evaluation of visual analytics systems. Computer Graphics Forum, 38(3):131-144, 2019. doi: 10.1111/cgf.13677

D. Sacha, M. Kraus, D. A. Keim, and M. Chen. VIS4ML: An ontology for visual analytics assisted machine learning. IEEE Transactions on Visualization and Computer Graphics, 25(1):385-395, 2019. doi: 10.1109/TVCG.2018.2864838

G. K. L. Tam, V. Kothari, and M. Chen. An analysis of machine- and human-analytics in classification. IEEE Transactions on Visualization and Computer Graphics, 23(1):71-80, 2017. doi: 10.1109/TVCG.2016.2598829 (IEEE VAST 2016 Best Paper Award)

Theories, Metrics and Empirical Studies

M. Chen and A. Golan, What May Visualization Processes Optimize? IEEE Transactions on Visualization and Computer Graphics, 22(12):2619-2632, 2016. doi: 10.1109/TVCG.2015.2513410

M. Chen, M. Feixas, I. Viola, A. Bardera, H.-W. Shen, M. Sbert. Information Theory Tools for Visualization. A K Peters/CRC Press, 2016. ISBN: 9781498740937

N. Kijmongkolchai, A. Abdul-Rahman, and M. Chen. Empirically measuring soft knowledge in visualization. Computer Graphics Forum, 36(3):73-85, 2017. doi: 10.1109/MCG.2017.3271463

Video Visualization

B. Duffy, J. Thiyagalingam, S. Walton, D. J. Smith, .A. Trefethen, J. C. Kirkman-Brown, E. A. Gaffney and. M. Chen, Glyph-based video visualization for semen analysis, IEEE Transactions on Visualization and Computer Graphics, 21(8):980-993, 2015. doi: 10.1109/TVCG.2013.265

R. P. Botchen, S. Bachthaler, F. Schick, M. Chen, G. Mori, D. Weiskopf and T. Ertl, Action-based multi-field video visualization, IEEE Transactions on Visualization and Computer Graphics, 14(4):885-899, 2008. doi: 10.1109/TVCG.2008.40, Patent: Europe EP2112619, US 20090278937 A1, 2009.

Volume Graphics and Volume Visualization.

C. Correa, D. Silver and M. Chen, Feature aligned volume manipulation for illustration and visualization, IEEE Transactions on Visualization and Computer Graphics, 12(5):1069-1076, 2006. doi: 10.1109/TVCG.2006.144

M. Chen and J. V. Tucker, Constructive volume geometry, Computer Graphics Forum, 19(4):281-293, 2000. doi: 10.1111/1467-8659.00464

Between 1992 and 2016, under Min Chen’s supervision or co-supervision, some 25 students have obtained their PhD or DPhil degrees, and three obtained their MPhil degrees. The research topics, on which Min Chen is currently supervising or will supervise DPhil projects, include:

• theoretical foundations of data visualization and visual analytics (e.g., information theory, cost-benefit measure, validation of visualization guidelines, etc.);
• techniques of data visualization (e.g., video visualization, event visualization, glyph-based visualization, spatiotemporal data visualization, etc.);
• optimization of visual analytics (e.g., integrated data intelligence, knowledge-assisted visual analytics, optimization methodologies, etc.);
• human-assisted machine learning (e.g., active learning, testing, quality assurance, rapid model development, etc.);
• information theory for underpinning data science;
• perception and cognition in data visualization and visual analytics;
• application-driven data intelligence (e.g., digital humanities, medicine and healthcare, bioinformatics, sports, cybersecurity, industry and business workflows, etc.)

If you are interested in studying DPhil on any of the above topics, please feel free to contact me directly to decide a topic that you will enter into your application.