Waddah Saeed

Waddah Saeed is a Senior Lecturer and Program Lead for the MSc Data Analytics at the School of Computer Science and Informatics, De Montfort University (DMU). He is actively involved in research, teaching, and external academic service, with affiliations including the Institute of Artificial Intelligence (IAI) and the Centre for Computing and Social Responsibility (CCSR).

His educational background includes a PhD in Information Technology from Universiti Tun Hussein Onn Malaysia (2019), a Master in Computer Science (Soft Computing), and a BSc in Computer Science. He previously served as a Postdoctoral Research Fellow at the University of Agder, Norway, and as a Lecturer at Asia Pacific University of Technology & Innovation, Malaysia.

His research expertise lies in time series analysis and forecasting, machine learning, and explainable AI, with applications in renewable energy and hierarchical forecasting. He is particularly interested in Graph Neural Networks for time series and feature importance in explainable AI models. His teaching includes courses such as Data Mining, Research Methods, Advanced Data Analytics, and Business Intelligence across BSc and MSc programs.

He is an active contributor to the academic community, serving as an External Examiner for Abertay University, an Independent Assessor for the University of Nottingham, and an External Academic Advisor for Birmingham City University. He also reviews grants for the Dutch Research Council and acts as a peer reviewer for publishers including Elsevier, Springer, MDPI, Hindawi, and IET.

His professional recognitions include:

• Gold Award (Publication Category), Universiti Tun Hussein Onn Malaysia, 2019
• Best Paper Award, 3rd International Conference of Reliable Information and Communication Technology, 2018
• Best Paper Award, 2nd International Conference on Soft Computing in Data Science, 2016

He holds the Fellowship of the Higher Education Academy (FHEA) and has earned certifications in university pedagogy and deep learning. He is currently leading internally funded research projects on divergence in explainable AI methods and solar forecasting. He serves as Guest Editor for a Special Issue on Intelligent Energy Forecasting in Applied Sciences (MDPI). Prospective PhD students with funding and aligned research interests are encouraged to contact him.