Staff profile

Jennifer is a doctoral researcher in the Department of Engineering. Her research focus lies in interdisciplinary approaches to knowledge engineering in wind energy. Jennifer held an Office for Students scholarship for her MSc and her doctoral research is funded by the Ørsted Doctoral Studentship. 

With a liberal arts undergraduate degree and an MSc Applied Data Science, she is interested in data science for global good. She was a delegate to the United Nations Commission on the Status of Women in 2023 and 2024, with a particular interest in how data science and technology can support progression towards the Sustainable Development Goals. 

Jennifer is a Fellow of Durham Energy Institute, an active participant in IEA Wind Task 43/66 (Digitalisation), and is passionate about collaborative, sociotechnical and interdisciplinary approaches to data challenges. Under the umbrella of IEA Wind Task 66, she leads a new working group 'Community Building and Collaborative Knowledge Ecosystems', which focuses on the role of collaboration and professional learning communities in the field of wind energy digitalisation.

Before starting her PhD, Jennifer built a 15 year career across a range of education fields. She continues this passion for teaching by lecturing part time on postgraduate courses in Teesside University’s School of Computing, Engineering and Digital Technologies, where her teaching interests lie in data visualisation and the ethics of artificial intelligence. 

 

Research Project

Jennifer’s doctoral research is part of an industrial collaboration with Ørsted, focusing on data mining to enhance condition monitoring of wind turbines. Improving operational efficiency and reducing downtime makes the case for increased implementation of wind energy as part of a drive towards Net Zero targets.

Journal Article

• Laying the foundations for context-aware and AI-ready fault diagnosis with the Operations Ontology
  
  Jonsson, C., Hansen, M. S., Wilson, J. J., Farren, D., Dethlefs, N., Chatterjee, J., Draper, J., Donnelly, R., Dimopoulos, A., Wiens, M., Marykovskiy, Y., & Barber, S. (2026). Laying the foundations for context-aware and AI-ready fault diagnosis with the Operations Ontology. Journal of Physics: Conference Series, 3232(1), Article 012005. https://doi.org/10.1088/1742-6596/3232/1/012005