Student Resources

Mai H. Elshehaly

Research Fellow
School of Computing
University of Leeds, UK
Curriculum Vitae
Google Scholar Profile

I am currently a research fellow at the University of Leeds, working on developing and evaluating a typology-driven framework for the automated adaptation of healthcare quality dashboards. I completed my Ph.D in computer science at Virginia Tech, where I worked at the Center for Human Computer Interaction (CHCI). I then spent one year as a postdoctoral research associate at University of Maryland, Baltimore County working in the Interactive Visual Computing Lab. My primary research interests are in computer graphics and scientific data visualization (SciVis). I am also interested in machine learning, visual analytics, and human computer interaction.


My research focuses on scientific data visualization, an area at the intersection of computer graphics, data analytics, and human-computer interaction. The goal is to enable domain experts to understand their data in order to steer the analysis, rather than use black-box machine learning and statistical techniques. We promote the human-in-the-loop principle in analyzing different types of scientific data, in which the human brain and the computer act together as parallel processors. The human expert forms hypotheses, asks questions, interacts with the visualization, then asks more questions and forms new hypotheses. This exploratory process poses several challenges on the computer, mainly due to the interactivity requirement despite the large size of available data.

My focus has been on two main types of scientific data. The first is time-varying volumetric datasets, typically used in application domains like atmospheric data analysis and environmental studies. The second type of data is large hierarchical networks, which are common in life sciences in the form of biological pathways. Each type of data poses a set of challenges that is addressable through a different pool of visualization techniques. I have developed novel interactive techniques to cater for both types.


Current Projects

Coming soon

Selected Publications

M. Elshehaly , N. Alvarado, L.McVey, R. Randell, M. Mamas, R.Ruddle
From Taxonomy to Requirements: A Task Space Partitioning Approach
BELIV Workshop 2018.
Supplementary material.

R. Spletchna, A. Diehl, M. Elshehaly , C.Delrieux, D. Gracanin and Kresimir Matkovic
Bus Lines Explorer: Interactive Exploration of Public Transportation Data. 2016.
In Proceedings of the 9th International Symposium on Visual Information Communication and Interaction (VINCI '16). , ACM, New York, NY, USA, 30-34. DOI:

M. Elshehaly, G. Szeto, Z. Pan, and J. Chen
ImmunoExplorer: A Web-based Multivariate Visualization Tool for Exploratory Analysis of Immunotherapy.
Proceedings of the International Conference on Virtual Reality and Visualization, September 2016. HamgZhou, China.

L. Cibulski, D. Gračanin, A. Diehl, R. Splechtna, M. Elshehaly, C. Delrieux, and K. Matkovic
ITEA—interactive trajectories and events analysis: exploring sequences of spatio-temporal events in movement data
The Visual Computer (2016) 32: 847. doi:10.1007/s00371-016-1255-7.

J. Wang, M. Elshehaly, Y. Cao.
Cylindrical Acceleration Structures for Large Hexahedral Volume Visualization.
Large Data Analysis and Visualization (LDAV), IEEE 5th Symposium on, pp 25-31, 2015.

M. Elshehaly, D. Gračanin, M. Gad, H. Elmongui, and K. Matkovi´c.
Interactive Fusion and Tracking For Multi-Modal Spatial Data Visualization.
Computer Graphics Forum (2015), – DOI 10.1111/cgf.12637 (Proc. EuroVis 2015).

R. Splechtna, M. Elshehaly, D. Gračanin, M. Duras, K. B¨uhler, and K. Matkovi´c.
Interactive interaction plot, supporting parameter space exploration in a design phase.
The Visual Computer. , 2015.

PhD Dissertation

Master's Thesis