Visual Analytics Summer School 2012, London, UK

“Graph Layout Methods and Their Visual Perception”, Visual Analytics Summer School, August 2012, London, UK

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Reviewer, IBM Journal of Research and Development (Impact factor 4.8)

Reviewer, IBM Journal of Research and Development (Impact factor 4.8).

A User Study on Curved Edges in Graph Visualisation

Kai Xu, Chris Rooney, Peter Passmore, and Dong-Han Ham, “A User Study on Curved Edges in Graph Visualisation”, Seventh International Conference on the Theory and Application of Diagrams, 2-6 July 2012, Canterbury, UK. PDF

Recently there has been increasing research interest in visualizing graphs with curved edges to produce more readable visualizations. While there are many automatic techniques, little has been done to evaluate them empirically. In this paper we present our study on the impact of edge curvature on graph readability by comparing the path finding performance on graph visualizations with varied curvature levels. We also asked the participants to provide subject ratings of the effectiveness and aesthetics of different types of curved edge.

2012-2013, Co-Investigator, Data Intensive Visual Analytics (DIVA), £220,000

This is jointly funded by the UK Research Council EPSRC (Engineering and Physical Sciences Research Council) and Defence Science and Technology Laboratory (dstl).

diva-screenshot

Besides the Middlesex University, there are three other partners in the research consortium: City University (principle investigator), Loughborough University, and Defence Science and Technology Laboratory (dstl).

The project aims to design and develop novel Visual Analytics techniques to address the data quality and uncertainty issues commonly encountered in Social Media analysis. Examples of such issues include the reliability of data source, errors and missing data, artefacts introduced by the analysis algorithms, and human bias in the decision making process. An important part to the solution will be the ‘provenance’, which is the detailed information of the entire decision making life cycle from data collection to reasoning and sense making. Provenance is expected to play a key role in identifying and tracking the data quality and uncertainty issues mentioned previously.

The initial application will be Human Terrain Analysis that provides Army troops with social insights into local population by integrative analysis of social media (e.g., Twitter) together with a range of other intelligence information (e.g., demographics). The results of Human Terrain Analysis will allow the ground troops to choose the appropriate engagement strategy with local communities and avoid any unnecessary confrontation and loss of life. The research outcomes are expected to later extend to other domains such cyber security, finance, and public health.

Phong Nguyen, PhD, Middlesex University, UK

2011-, adviser, “Spatial-Temporal Analysis of Online Social Information and its Provenance using Visual Analytics”, School of Engineering and Information Sciences, Middlesex University, UK.

Reviewer, IEEE Pacific Visualization 2012

Reviewer, IEEE Pacific Visualization 2012.

Reviewer, Computers & Graphics Journal (Impact Factor 0.72)

Reviewer, Computers & Graphics Journal (Impact Factor 0.72) by Elsevier.