School of Computing

Staff Profiles

Dr Sara Fernstad



I am a Lecturer in Data Science with ten years' experience of Information Visualization research. Before joining the School of Computing Science 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 research interests are in the area of Information Visualization and particularly in challenges relating to incomplete (missing), high dimensional and heterogeneous data, visualization of biomedical and ‘omics-type’ data, and the combination of visualization and data mining methods.


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 data mining methods and user-centred design approaches. 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 crosses 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?

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