Staff profile

Jennifer is a doctoral researcher in the Department of Engineering. She holds an MSc Applied Data Science with distinction and her research focus lies in applying deep learning methodologies to wind energy applications. 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. 

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.