Thought Leadership Event: Prof M Jones, Prof D Archambault, Prof X Xie on "Data Visualisation Techniques"

Date: 26th November 2020, 13:30 - 14:30
Speakers: Professors from the Computational Foundry at Swansea University.
Title: Data Visualisation Techniques 
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The event was hosted by BT Applied Research's Dr Anasol Pena Rios (2020 WeAreTechWomen100 Award winner).

Prof Mark Jones

"Interesting algorithms, techniques and visualisations!"

In the past few years I’ve been interested in developing new approaches for rendering, accelerating algorithms and immersing human experts in their data. Some of the areas I’ve been deeply involved with have involved the following techniques: photon mapping, ray tracing, global illumination, visualization, kernel density estimation, accelerometry data, Machine Learning, lossless data compression, transfer functions, probability density functions, clustering, Monte-Carlo techniques, statistics, GPU acceleration, distance measures, Lloyd’s relaxation, Voronoi diagrams, vector/chamfer distances, volume rendering, data structures (kd-trees). 

I will show some work in these areas and adapt the talk as we go along depending on the kinds of research you’d like to hear about.

Mark W. Jones received B.Sc. and Ph.D. degrees from Swansea University. He is a Professor in the Department of Computer Science at Swansea University. His research interests include global illumination, visualization, data science, and associated algorithms and data structures.

He has published over 25 journal papers since 2013 in the area of Visual Computing, Data Analysis, and Machine Learning, received Best Paper and ACM Computing Reviews Best of 2013 awards. He has been an investigator on six EPSRC projects (including three multi-site, and four as PI in the complementary areas of Computer Graphics and Visualization) and has been an active member of the UK Visual Computing community for 25 years, including programme chair of BMVC 2015 and conference chair of the EGUK conference in 2002 and 2003. He has over 80 papers in the area of Visual Computing, and a patent.

Daniel Archambault
"Visualising and Clustering Networks and Text"

Networks and text are important data types in data science, but as their scale increases it is difficult to visualise all of the information directly. 

In this presentation, I present some of my research on visualising and clustering this information for effective analysis. In particular, an analysis of the Irish blogosphere and an experiment to compare automatically detected and human-generated clusters in social media networks.

Daniel Archambault is an Associate Professor of Computer Science at Swansea University. His main area of research is visual analytics, in particular for networks, and human-centred perspectives of visualisation. In this talk, he will present some projects on network and document clustering and visualisation. 

Prof Xianghua Xie

Visual Learning and Graph Deep Learning” 

Xianghua Xie is a Professor in the Visual Computing Group at the Department of Computer Science, Swansea University. He was a recipient of an RCUK Academic Fellowship (tenure-track research-focused lectureship) between September 2007 and March 2012. He was appointed as a Senior Lecturer from October 2012, then an Associate Professor in April 2013, and a full Professor from March 2019. Prior to his position at Swansea, he was a Research Associate in the Computer Vision Group, Department of Computer Science, University of Bristol, where he completed both his PhD (2006) and MSc (2002) degrees.

Professor Xie has strong research interests in the areas of Pattern Recognition and Machine Intelligence and their applications to real-world problems. He has been an investigator on several research projects funded by external bodies, such as EPSRC, Leverhulme, NISCHR, and WORD. 

Among his research works, those of significant importance include detecting abnormal patterns in complex visual and medical data, assisted diagnosis using automated image analysis, fully automated volumetric image segmentation, registration, and motion analysis, machine understanding of human action, efficient deep learning, and deep learning on irregular domains. 

By 2020, he has published over 150 fully refereed research publications and (co-)edited several conference proceedings. He is an associate editor of IET Computer Vision and an editorial member of a number of other international journals and has chaired and co-chaired several international conferences, e.g. BMVC2015 and BMVC2019. More information is here