Debbie Taylor

Debbie joined the UEA in September 2014 as an undergraduate student in Computing Sciences, during which she worked as an Associate Tutor. Following successful completion of her First-Class Honours degree, in 2018 she changed career path and became a full time Tutor/Lecturer in the School of Computing Science.

Her current responsibilities include teaching and organising multiple modules, for both undergraduate and postgraduate students, across a wide range of topics, including Cyber Security, Software Engineering, Python programming, Information Retrieval, Human Computer Interaction and User Experience (UX).

Alongside, her teaching roles she holds the role of Senior Adviser for the school of Computing Sciences, is a member of the Equality and Diversity committee and Cyber Security research group and chairs the Staff Student Liaison Committee, to ensure students possess a voice in how the school is run. She is also the developer and facilitator of Big Sister Little Sister, a mentoring group for female identifying students in Computing Science, as she is passionate about improving and promoting gender equality in a male dominated environment.  

Prior to joining the University of East Anglia (UEA) Deborah (Debbie) Taylor spent 25 years working for a global insurance company and was involved in multiple software engineering and information technology projects, from basic testing through to co-project management. During this time, she became a fully qualified Quality Assurance Technician and Software Engineer, and this led to her performing consultancy roles across multiple companies with headquarters in both the UK and Europe.

Her aim is to be use her prior industry and consultancy skills to support her teaching, by discussing real world examples of what does and doesn't work in industry.

Debbie is currently working towards a part time PhD in Cyber Security, researching sociolinguistics, psycholinguistics, and computational linguistics for sensitive data disclosures via conversational agents (chatbots). The overall aim of the research is to identify if chatbots can be trusted enough to support routine sensitive data collection tasks for initial mental health disclosures. The first study established that humans believe chatbots can, and should, exhibit a range of different human-like attributes, characteristics, and features. The second study evidenced that a bespoke chatbot, using these conversational requirements, was more successful at establishing trust, than standard online forms, especially for certain demographics such as age ranges 18-39 and people identifying as non-binary. The final study will evaluate the most effective linguistic approaches for maximising this trust, and identify if this collection tool can help mental health practioners develop a quicker and more holistic, relationship with their patients or aid in identifying treatment plans.

• Senior Adviser, supporting students and faculty within the School of Computing Science.
• Member of the Computing Science Cyber Security Research Group
• Member of the Computing Science Equalities and Diversity committee 
• Member of the Computing Science Executive Team 
• Advising and supporting students as an Academic Advisor and Senior Adviser 
• Developer and facilitator of Big Sister Little Sister, a mentoring group for female identifying students in Computing Science, as she is passionate about improving and promoting gender equality in a male dominated environment.