Dr Sara Johansson Fernstad

Roles: Senior Lecturer in Data Science
Research Group: Scalable Computing
Email: sara.fernstad@newcastle.ac.uk

Sara is a Senior Lecturer in Data Science with expertise in visualization and is Newcastle University’s academic lead for the Turing University Network.

About me

  • My name is pronounced Saa-ra Yo-han-son Fern-staad
  • I speak 🇸🇪 and 🇬🇧
  • My pronouns are she/her

Bio

Sara received a PhD from Norrköping Visualization Centre-C, Linköping University, Sweden, in 2011, with a thesis titled Algorithmically Guided Information Visualization: Explorative Approaches for High Dimensional, Mixed and Categorical Data. Following her PhD she held positions as Data Visualization Scientist at Unilever R&D Port Sunlight (2011-2013), and as PostDoc in Data Visualization at the Unilever Centre for Molecular Science Informatics at Cambridge University, UK (2013-2014). This was followed by a lectureship in Computer Science at Northumbria University, UK (promoted to Senior Lecturer 2016). She was appointed Lecturer in Data Science at Newcastle University, UK, in 2017.

Research

Sara’s main interests are in research challenges relating to visualization of incomplete (missing), uncertain, high dimensional and/or heterogeneous data; visualization of biomedical and ‘omics-type’ data; visualization in data profiling; and human centred visualization design. These challenges are all highly relevant in a range of application domains, not least in biomedical domains and life sciences, which are becoming more and more data driven. Her work on interactive visual dimensionality reduction in collaboration with microbiologists has been applied in industry and academia, leading to eight joint publications and funding of two PhD studentships.

Methodologically, as an overarching theme to Sara’s research lies the concept of ‘interestingness’. How do we bring out the most interesting and useful information in a dataset? How do we define what is interesting? How can we use ‘interestingness’ to support knowledge discovery and gaining of insights in the most useful way? At the heart of this lies the human who need to understand and make decisions based on complex data, and to address and define their analytical needs through the utilisation of user and task centred design approaches. 

Publications

  • Holliman, N. S., Çöltekin, A., Fernstad, S. J., McLaughlin, L., Simpson, M. D., & Woods, A. J. (2024). Entropy ordered shapes as bivariate glyphs. Electronic Imaging, 36, 1-10. https://doi.org/10.2352/EI.2024.36.11.HVEI-206

  • Alsufyani, S., Forshaw, M., Del Din, S., Yarnall, A., Rochester, L., & Fernstad, S. J. (2024). Multi-level visualization for exploration of structures in missing data. CGVC. The Eurographics Association.

  • Ruddle, R., Cheshire, J. & Johansson Fernstad, S. (2024). A Practical Guide to Characterising Data and Investigating Data Quality. University of Leeds. https://doi.org/10.5518/1481

  • Alzahrani, H. & Johansson Fernstad, S. (2023). An investigation into various visualization tools for complex biological networks. Information Visualization, 22(4), 323-339. https://doi.org/10.1177/14738716231181545

  • Romijnders, R., Salis, F., Hansen, C., Küderle, A., Paraschiv-Ionescu, A., Cereatti, A., Alcock, L., Aminian, K., Becker, C., Bertuletti, S., Bonci, T., Brown, P., Buckley, E., Cantu, A., Carsin, A.-E., Caruso, M., Caulfield, B., Chiari, L., D’Ascanio, I., … Maetzler, W. (2023). Ecological validity of a deep learning algorithm to detect gait events from real-life walking bouts in mobility-limiting diseases. Frontiers in Neurology, 14, 1247532. https://doi.org/10.3389/fneur.2023.1247532

  • Ruddle, R. A., Cheshire, J., & Johansson Fernstad, S. (2023). Tasks and Visualizations Used for Data Profiling: A Survey and Interview Study. IEEE Transactions on Visualization and Computer Graphics. https://doi.org/10.1109/TVCG.2023.3234337

  • Cantu, A., Micó-Amigo, M. E., Del Din, S., & Fernstad, S. J. (2023). Parallel Assemblies Plot, a visualization tool to explore categorical and quantitative data: Application to digital mobility outcomes. 2023 IEEE 16th Pacific Visualization Symposium (PacificVis), 21–30. https://doi.org/10.1109/PacificVis56936.2023.00010

  • Micó-Amigo, M. E., Bonci, T., Paraschiv-Ionescu, A., Ullrich, M., Kirk, C., Soltani, A., Küderle, A., Gazit, E., Salis, F., Alcock, L., Aminian, K., Becker, C., Bertuletti, S., Brown, P., Buckley, E., Cantu, A., Carsin, A.-E., Caruso, M., Caulfield, B., … for the Mobilise-D consortium. (2023). Assessing real-world gait with digital technology? Validation, insights and recommendations from the Mobilise-D consortium. Journal of NeuroEngineering and Rehabilitation, 20(1), 78. https://doi.org/10.1186/s12984-023-01198-5

  • Macquisten, A., Smith, A. M., & Johansson Fernstad, S. (2022). Hierarchical Visualization for Exploration of Large and Small Hierarchies. In Integrating Artificial Intelligence and Visualization for Visual Knowledge Discovery (pp. 587-612). Springer, Cham. https://link.springer.com/chapter/10.1007/978-3-030-93119-3_23

  • Koc, K., McGough, A. S., & Johansson Fernstad, S. (2022). PeaGlyph: Glyph design for investigation of balanced data structures. Information Visualization, 21(1), 74-92. https://doi.org/10.1177/14738716211050602

  • Garner, H. & Johansson Fernstad, S. (2022) Comparative Evaluation of the Scatter Plot Matrix and Parallel Coordinates Plot Matrix. In 2022 26th International Conference on Information Visualization (IV). IEEE. https://doi.org/10.1109/IV56949.2022.00027

  • Holliman, N. S., Coltekin, A., Fernstad, S. J., McLaughlin, L., Simpson, M. D., & Woods, A. J. (2022). Visual entropy and the visualization of uncertainty (arXiv:1907.12879). arXiv. https://doi.org/10.48550/arXiv.1907.12879

  • Johansson Fernstad, S., & Johansson Westberg, J. (2021). To Explore What Isn’t There—Glyph-Based Visualization for Analysis of Missing Values. IEEE Transactions on Visualization and Computer Graphics, 28(10), 3513-3529. https://doi.org/10.1109/TVCG.2021.3065124

  • Johansson Fernstad, S., Macquisten, A., Berrington, J. E., Embleton, N. D., & Stewart, C. J. (2020). Quality Metrics to Guide Visual Analysis of High Dimensional Genomics Data. In EuroVA@ Eurographics/EuroVis (pp. 31-35). https://doi.org/10.2312/eurova.20201083

  • Macquisten, A., Smith, A. M., & Johansson Fernstad, S. (2020, September). Evaluation of hierarchical visualization for large and small hierarchies. In 2020 24th International Conference Information Visualisation (IV) (pp. 166-173). IEEE. https://doi.org/10.1109/IV51561.2020.00036

  • Johansson Fernstad, S. (2019). To identify what is not there: A definition of missingness patterns and evaluation of missing value visualization. Information Visualization, 18(2), 230-250. https://doi.org/10.1177/1473871618785387

  • Grube, M., Gaya, E., Kauserud, H., Smith, A. M., Avery, S. V., Fernstad, S. J., … & Bendiksby, M. (2017). The next generation fungal diversity researcher. Fungal Biology Reviews, 31(3), 124-130. https://doi.org/10.1016/j.fbr.2017.02.001

Google Scholar profile

PhD supervision (as main supervisor)

  • Hugh Garner (start April 2018, EPSRC), Interactive explorative visual analytics for hierarchical meta-‘omics data.
  • Lama Alsmmahi (start Nov 2022), Multi-omics Data Visualization for Paediatric Cancer Patients.
  • Fatimah Alqahtani (start Sep 2023), Adaptive Colormap Optimization for Enhanced Perceptual Clarity.

Please get in touch to discuss opportunities and project ideas for PhD research with visualization focus.

Past PhD supervision

  • Dr Sarah Alsufyani (2019-2026), thesis: Visualisation for investigation of structural missingness: Application to the ICICLE Mobility Dataset.
  • Dr Hanin Alzahrani (2020-2024), thesis: Improving the usability of complex biological networks through interestingness measures and interactive visualization.
  • Dr Kenan Koc (2018-2022), thesis: Information Visualization Approach to Form Balanced Groups.
  • Dr Alexander Macquisten (2017-2022, BBSRC & Unilever R&D), thesis: Hierarchical Visualization of High Dimensional Data – Interactive Exploration of ’Omics Type Data.
  • Lucy McLaughlin (start Sep 2021), Using Shape for Uncertainty Representation in Multivariate Data.
  • Dr Halil Agin (2016-2019, Northumbria University), thesis: Extracting Knowledge from Statistical Model by Brushing in Parameter Space

Teaching

Sara is the module leader of the following modules in the School of Computing, Newcastle University

  • Complex Data Visualization (CSC8636) – MSc Data Science
  • Data Visualization and Visual Analytics (CSC3833) – BSc Computer Science (3rd year)

In 22/23 she was also the module leader of:

  • Data Visualization (CSC8626) – MSc Data Science
  • Data Visualization (CSC8642) – MSc Digital and Technology Solutions (Data Analytics) Degree Apprenticeship