A Framework for Machine Learning-aided Visualisation to support Collaborative Decision Making


   Faculty of Engineering & Digital Technologies

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  Dr Mai Elshehaly  Applications accepted all year round  Self-Funded PhD Students Only

About the Project

In group decision making, a number of individuals collaboratively assess a competing set of alternatives to select the optimal solution for a problem. This process is complicated by factors including: the complexity of alternatives under consideration, poor data quality (e.g. timeliness, completeness, etc.), time limitations and the lack of support for adequate communication between collaborators. Machine learning has the potential to direct decision makers’ attention to critical data facets while reducing the complexity and dimensionality of the data. Visualisation has been frequently used as a medium for effective communication of information in order to facilitate collaboration. In this PhD project, we will explore a combination of machine learning and visualisation techniques to support group decision making. We will develop a framework for the efficient interaction between human-led and computer-guided analysis, and explore computational and cognitive implications of this analysis, when conducted in a group setting. Our research will focus on dynamics for collaboration in the post COVID-19 era, in which technology will supplement a wide variety of face-to-face interaction scenarios to maintain measures of social distancing.

As a PhD researcher, you will have the opportunity to work in close collaboration with an interdisciplinary group of stakeholders and researchers. In addition to academic networking opportunities (e.g. attending conferences, organising workshops, etc.), this project presents several networking opportunities through requirements elicitation and co-design activities.
Computer Science (8)

References

1. Elshehaly, M., Alvarado, N., McVey, L., Randell, R., Mamas, M., & Ruddle, R. A. (2018, October). From Taxonomy to Requirements: A Task Space Partitioning Approach. In Proceedings of the IEEE VIS Workshop on Evaluation and Beyond–Methodological Approaches for Visualization (BELIV). IEEE.

2. Splechtna, R., Elshehaly, M., Gračanin, D., Ɖuras, M., Bühler, K., & Matković, K. (2015). Interactive interaction plot. The Visual Computer, 31(6-8), 1055-1065.

3. Hindalong, E., Johnson, J., Carenini, G., & Munzner, T. (2020, June). Towards Rigorously Designed Preference Visualizations for Group Decision Making. In 2020 IEEE Pacific Visualization Symposium (PacificVis) (pp. 181-190). IEEE.

4. Sedlmair, M., Meyer, M., & Munzner, T. (2012). Design study methodology: Reflections from the trenches and the stacks. IEEE transactions on visualization and computer graphics, 18(12), 2431-2440.

Where will I study?

 About the Project