I am a Lecturer in Data Science with over ten years' experience of Visualization research, and a Fellow of the Alan Turing Institute. Before joining the School of Computing at Newcastle University, I was a Senior Lecturer in Computer Science at University of Northumbria at Newcastle. Prior to that I held post-doctoral research positions at Cambridge University, UK, and at Unilever R&D, UK. I completed my PhD on algorithmically guided visualization for analysis of high dimensional and heterogeneous data in 2011, at Linköping University in Sweden.

My 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 visualization design.

Current PhD supervision:

• Information Visualization Approach to Form Balanced Groups. Kenan Koc
• Interactive visualization of temporal multi-omic data. Hugh Garner. EPSRC
• Visualization for investigation of data quality in health data. Sarah Hamed H Alsufyani
• Improving the usability of complex biological networks through interestingness measures and interactive visualization. Hanin Saeed A Alzahrani.
• Using Shape for Uncertainty Representation in Multivariate Data. Lucy McLaughlin

Past PhD supervision:

• Hierarchical Visualization of High Dimensional Data: Interactive Exploration of 'Omics Type Data. Alexander Macquisten. BBSRC & Unilever R&D - Thesis defence 4 April 2022

Google Scholar: Click here.

My main research interests are in the field of Information Visualization (Visual Analytics/Data Visualization) and I am particularly interested in challenges relating to high dimensional data, heterogeneous data and uncertainty in data. 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.

From a methodological point of view, my interests are focused around the combination of visualization and and data mining methods through semi-automated approaches and user-centred design. Visualization and data mining both address the same type of challenges, with similar goals but with slightly different approaches and strengths. By combining the two we may be able to find better solutions to major data analysis challenges.

My research to date can broadly be separated into four major themes, which ever so often cross each other’s paths: Visualization of Missing Data, Visual Exploration of Microbial Populations, Interactive Visual Exploration of High Dimensional Data, and Visualization of Heterogeneous Data. As an overarching theme to this 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?

I am currently Co-Investigator of the following funded projects:

• Connecting digital mobility assessment to clinical outcomes for regulatory and clinical endorsement (Mobilise-D) (Horizon 2020 Joint Technology Initiative, Innovative Medicine Initiative)
• Automating data visualisation (Alan Turing Institute)
  
• Visualising data profiles and analysis pipelines (Alan Turing Institute)

Google scholar profile

Invited talks:

• To See What Isn't There - Visualisation of Missing Data. Norrkoping Visualization Centre - C, Linkoping University, Sweden. April 2019.
• Designing Interactive Visual Analytics Tools for Exploration of Microbial Communities. Centre for Bioinformatics, Hamburg University, Germany. Jan 2018.
• To See What Isn't There - Visualisation of Missing Data. Unilever R&D Colworth, UK. Oct 2017.
• To See What Isn't There - Visualisation of Missing Data. School of Computing Science, University of Glasgow, UK. April 2017.
  
• To See What Isn't There - Visualisation of Missing Data. School of Computing Science, University of Leeds, UK. Feb 2015.
• Designing Visual Analytics Tools for Exploration of Microbial Ecology. Oxford e-Research Centre, University of Oxford, UK. May 2014.

2021/2022

• CSC8636 - Complex Data Visualization (module leader)
• CSC1031 - Fundamentals of Computing

2022/2023

• CSC3833 - Data Visualization and Visual Analytics (module leader)
  
• CSC8626 - Data Visualization (module leader)
  
• CSC8636 - Complex Data Visualization (module leader)
  

• Johansson Fernstad S, Johansson J. To Explore What Isn’t There — Glyph-based Visualization for Analysis of Missing Values. IEEE Transactions on Visualization and Computer Graphics 2021, (ePub ahead of Print).
• Johansson Fernstad S, Macquisten A, Berrington J, Embleton N, Stewart C. Quality Metrics to Guide Visual Analysis of High Dimensional Genomics Data. In: EuroVis Workshop on Visual Analytics (EuroVA). 2020, Norrköping, Sweden: The Eurographics Association.
• Fernstad SJ. To identify what is not there: A definition of missingness patterns and evaluation of missing value visualization. Information Visualization 2019, 18(2), 230-250.
• Grube M, Gaya E, Kauserud H, Smith AM, Avery SV, Fernstad SJ, Muggia L, Martin MD, Eivindsen T, Kõljalg U, Bendiksby M. The next generation fungal diversity researcher. Fungal Biology Reviews 2017, 31(3), 124-130.
• Stewart CJ, Skeath T, Nelson A, Fernstad SJ, Marrs ECL, Perry JD, Cummings SP, Berrington JE, Embleton ND. Preterm gut microbiota and metabolome following discharge from intensive care. Scientific Reports 2015, 5, 17141.
• Stevens D, Cornmell R, Taylor D, Grimshaw SG, Riazanskaia S, Arnold DS, Fernstad SJ, Smith AM, Heaney LM, Reynolds JC, Thomas CLP, Harker M. Spatial variations in the microbial community structure and diversity of the human foot is associated with the production of odorous volatiles. FEMS Microbiology Ecology 2015, 91(1), 1-11.
• Fernstad SJ, Glen RC. Visual analysis of missing data - To see what isn't there. In: IEEE Visual Analytics Science and Technology. 2014, Paris, France: IEEE.
• Fernstad SJ, Shaw J, Johansson J. Quality-based guidance for exploratory dimensionality reduction. Information Visualization 2013, 12(1), 44-64.
• Fernstad SJ, Johansson J, Adams S, Shaw J, Taylor D. Visual exploration of microbial populations. In: Biological Data Visualization (BioVis), 2011 IEEE Symposium on. 2011, Providence, RI, USA: IEEE.
• Fernstad SJ, Johansson J. A task based performance evaluation of visualization approaches for categorical data analysis. In: Information Visualisation (IV), 2011 15th International Conference on. 2011, London, UK: IEEE.
• Johansson S, Johansson J. Visual analysis of mixed data sets using interactive quantification. ACM SIGKDD Explorations Newsletter 2010, 11(2), 29-38.
• Johansson S, Johansson J. Interactive dimensionality reduction through user-defined combinations of quality metrics. IEEE Transactions on Visualization and Computer Graphics 2009, 15(6), 993-1000.
• Johansson S. Visual exploration of categorical and mixed data sets. In: ACM SIGKDD Workshop on Visual Analytics and Knowledge Discovery: Integrating Automated Analysis with Interactive Exploration. 2009, Paris, France: ACM.
• Johansson S, Knaving K, Lane A, Jern M, Johansson J. Interactive exploration of ingredient mixtures using multiple coordinated views. In: Information Visualisation, 2009 13th International Conference. 2009, Barcelona, Spain: IEEE.
• Johansson S, Jern M, Johansson J. Interactive quantification of categorical variables in mixed data sets. In: Information Visualisation, 2008. IV '08. 12th International Conference. 2008, London, UK: IEEE.
• Holliman NS, Coltekin A, Fernstad SJ, Simpson MD, Wilson KJ, Woods AJ. Visual Entropy and the Visualization of Uncertainty. arXiv 2019, arXiv:1907.12879v1.
• Koc K, McGough AS, Johansson Fernstad S. PeaGlyph: Glyph design for investigation of balanced data structures. Information Visualization 2022, 21(1), 74-92.