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Dr Kakia Chatsiou

Lecturer in Computing

Email
k.chatsiou@uos.ac.uk
School/Directorate
School of Technology, Business and Arts
Kakia Chatsiou ORCID
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Kakia is a Lecturer in Computing at the School of Technology, Business and Arts with expertise in machine learning, information management systems and data science. She holds a PhD from the University of Essex (2010). She is a member of the School of TBA Ethics Committee, the School of TBA Executive committee, and the OSACC committee at the University of Suffolk. Before joining Suffolk, she was a postdoctoral researcher at the University of Essex where she worked on automated, quantitative methods of processing large amounts of textual and other forms of unstructured data – mainly political texts and social media – and the methodology of Natural Language Processing.

Kakia is the module leader for the following modules:

  • Introduction to Programming (L4 mainstream)
  • Relational Databases (L5 mainstream)
  • Information Extraction (L6 mainstream & Apprenticeship route)
  • UG Dissertation Module (L6 mainstream)
  • Cloud Computing for Data Science and AI (L7)

My research focuses on automated, quantitative methods of processing large amounts of textual and other forms of unstructured data – mainly political texts and social media – and the methodology of text mining. My research is driven by my fascination with all kinds of data (big/small, text/number/images) and how it can help us better understand ourselves and our society. I am also interested in how data can be used to improve resilience, maximise accountability and trust and reduce inequalities.

Here is a list of recent publications:

Bonacaro, M, Chatsiou, K., Georgiadis, M., Monaco, A. (2023) Understanding chronic pain in the ubiquitous community: a blueprint for open data. In Frontiers in Pain Research. To appear.

Chamberlain, J., Turpin, B., Ali, M., Chatsiou, K., & O’Callaghan, K. (2021). Designing for Collective Intelligence and Community Resilience on Social Networks. Human Computation, 8(2), 15-32. https://doi.org/10.15346/hc.v8i2.116

Chatsiou, K. & Mikhaylov, S.J. (2020). Deep Learning for Political Science. The SAGE Handbook of Research Methods in Political Science and International Relations, pp.1053–1078. Available at: http://dx.doi.org/10.4135/9781526486387.n58

Chatsiou, K. (2020). Text Classification of COVID-19 Press Briefings using BERT and Convolutional Neural Networks. MS ArXiv:2010.10267 [Cs]. http://arxiv.org/abs/2010.10267 

Projects I work on leverage current technologies in machine learning, natural language processing and experimental behavioral study to describe the mechanisms for digital transformation in communities, businesses and the public sector and how data can be used as an enabler of a fairer, more transparent and accountable society.

I have worked with local authorities, charity organisations and businesses in the space above, such as MultiplAI, the LoadStar, TIPE, Cassius.

I am a STEM ambassador and I am occasionally invited to talk about my work and STEM in general in schools and FE colleges.