Professor Daniel Archambault

Role: Professor in Data Science, Scalable Group Co-Lead
Research Group: Scalable Computing
Email: daniel.archambault@newcastle.ac.uk

Daniel is a Professor of Visualization/Data Science. His research helps visualisation and visual analytics systems scale to the age of data science.

About me

  • My name is pronounced Dan-yel Ar-shan-bow
  • I am Canadian, British
  • I speak 🇬🇧 and 🇫🇷
  • My pronouns are he/him

Bio

I am a Professor of Visualisation/Data Science at Newcastle University. My research helps visualisation and visual analytics systems scale to the age of data science. From this perspective, I investigate important research problems in data science and AI, graph drawing, social and complex network analysis, and HCI often in interdisciplinary settings. I investigate all parts of the data-to-human pipeline from the visualisation algorithms to display abstract data to the perceptual evaluation of such interfaces with humans, primarily focusing on visual analytics for machine learning and network visualisation.

Publications

  • Watkin, L., Archambault, D., & Telea, A. (2026). Shapdbm: Exploring decision boundary maps in shapley space. https://doi.org/10.2312/EVS.20261004

  • Wallinger, M., Piselli, T., Tappini, A., Archambault, D., Liotta, G., & Nöllenburg, M. (2025). Bundling-aware graph drawing revisited. IEEE Transactions on Visualization and Computer Graphics, 31(12), 10828–10839. https://doi.org/10.1109/TVCG.2025.3616583

  • Baumgartl, T., Filipov, V., Rajendran, S., Miksch, S., Archambault, D., Arleo, A., & Von Landesberger, T. (2025). Layers of doubt: Typology of temporal uncertainty in dynamic diffusion networks. 2025 IEEE Workshop on Uncertainty Visualization: Unraveling Relationships of Uncertainty, AI, and Decision-Making, 53–57. https://doi.org/10.1109/UncertaintyVisualization68947.2025.00012 🏆 Best Paper Honourable Mention at VIS 2025 Uncertainty Workshop

  • Kozlikova, B., Archambault, D., Dreesman, J., Kerren, A., Lucini, B., & Turkay, C. (2025). Embarrassingly agile—Data visualization methodology in emergency responses. IEEE Computer Graphics and Applications, 45(5), 138–146. https://doi.org/10.1109/MCG.2025.3595342

  • Krawczyk, K., Tam, G. K. L., & Archambault, D. (2025). Financial forecasting in consumer cyclicals with economic indicators and tokenization. International Journal of Computer Theory and Engineering, 17(4), 179–188. https://doi.org/10.7763/IJCTE.2025.V17.1380

  • Almajyul, A., Archambault, D., & Forshaw, M. (2025). Are large screens effective at supporting the analysis of delay visualizations? 2025 29th International Conference Information Visualisation (IV), 13–18. https://doi.org/10.1109/IV68685.2025.00015

  • Riddell, J., Letina, S., Skivington, K., Archambault, D., Wells, V., Long, E., Hunter, R., & McCann, M. (2026). Methods for interventions using networks to improve health: A narrative synthesis of methodological research on network data collection, visualisation and intervention. Social Networks, 84, 202–219. https://doi.org/10.1016/j.socnet.2025.10.003

  • Jeon, H., Lee, H., Kuo, Y.-H., Yang, T., Archambault, D., Ko, S., Fujiwara, T., Ma, K.-L., & Seo, J. (2025). Navigating high-dimensional backstage: A guide for exploring literature for the reliable use of dimensionality reduction. EuroVis 2025 – Short Papers. https://doi.org/10.2312/EVS.20251087

  • Jeon, H., Lee, H., Kuo, Y.-H., Yang, T., Archambault, D., Ko, S., Fujiwara, T., Ma, K.-L., & Seo, J. (2025). Unveiling high-dimensional backstage: A survey for reliable visual analytics with dimensionality reduction. Proceedings of the 2025 CHI Conference on Human Factors in Computing Systems, 1–24. https://doi.org/10.1145/3706598.3713551

  • Wallinger, M., Akbulut, O., Rufai, K. A., Purchase, H. C., & Archambault, D. (2025). How do people perceive bundling? An experiment. Proceedings of the 2025 CHI Conference on Human Factors in Computing Systems, 1–14. https://doi.org/10.1145/3706598.3713444

  • Archambault, D. (2025). On the importance of visualisation in a data driven society: Proceedings of the 20th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications, 7–10. https://doi.org/10.5220/0013445500003912

  • Filipov, V., Ceneda, D., Archambault, D., & Arleo, A. (2025). Timelighting: Guided exploration of 2d temporal network projections. IEEE Transactions on Visualization and Computer Graphics, 31(3), 1932–1944. https://doi.org/10.1109/TVCG.2024.3514858

  • Archambault, D., Liotta, G., Nöllenburg, M., Piselli, T., Tappini, A., & Wallinger, M. (2024). Bundling-aware graph drawing. LIPIcs, Volume 320, GD 2024, 320, 15:1-15:19. https://doi.org/10.4230/LIPICS.GD.2024.15

  • Vago, B., Archambault, D., & Arleo, A. (2024). Dyntrix: A hybrid representation for dynamic graphs. Computer Graphics Forum, 43(3), e15076. https://doi.org/10.1111/cgf.15076

  • Rottmann, P., Rodgers, P., Yan, X., Archambault, D., Wang, B., & Haunert, J. (2024). Generating euler diagrams through combinatorial optimization. Computer Graphics Forum, 43(3), e15089. https://doi.org/10.1111/cgf.15089

  • Huang, Z., Archambault, D., Borgo, R., & Kerren, A. (2024). Matrix snap&go: Visualization of paths on matrices. EuroVis 2024 – Short Papers. https://doi.org/10.2312/EVS.20241058