Dr Waddah Saeed

 Waddah Saeed is a Senior Lecturer in Data Analytics and Program Lead for the MSc Data Analytics at the School of Computer Science and Informatics, De Montfort University (DMU). He earned his Ph.D. in Information Technology from Universiti Tun Hussein Onn Malaysia in 2019, receiving the Gold Award in the Publication Category for his thesis. Prior to joining DMU, he served as a Postdoctoral Research Fellow at the University of Agder in Norway and as a Lecturer at Asia Pacific University of Technology & Innovation (APU) in Malaysia. His research focuses on time series analysis and forecasting, machine learning, and explainable AI.

In addition to his academic roles, he actively contributes to global scholarship as an External Examiner, Advisor, and Assessor for some UK universities, including Abertay University, Birmingham City University, and the University of Nottingham. He also serves as an External Grant Reviewer, Guest Editor, and peer reviewer for leading publishers such as Elsevier and Springer, reflecting his commitment to advancing research and educational excellence.

• Institute of Artificial Intelligence (IAI)
• Centre for Computing and Social Responsibility (CCSR)

A full and updated list of publications can be found in the Google Scholar link below.
https://scholar.google.com/citations?user=2pqJUFEAAAAJ

• Time series analysis and forecasting
• Machine learning
• Explainable AI

Data Mining Techniques and Applications, Research Methods, Advanced Data Analytics, Data Visualisation, Business Intelligence, and Database Design.

• PhD in Information Technology.
• Master in Computer Science (Soft Computing).
• BSc in Computer Science.

MSc Data Analytics, Business Data Analytics BSc (Hons), Business Information Systems BSc (Hons), and BSc Applied Computing.

• Gold Award (Publication Category), Universiti Tun Hussein Onn Malaysia, Malaysia, 2019.
• Best Paper Award, 3rd international conference of reliable information and communication technology, Malaysia, 2018.
• Best Paper Award, 2nd international conference on soft computing in data science, Malaysia, 2016.

Fellow of the Higher Education Academy (FHEA), Advance HE-UK, 2023  - Present.

• Highfield Level 3 Award in Undertaking End-Point Assessment (RQF), 2025.
• University Pedagogy Course, University of Agder, Norway, 2022.
• NVIDIA DLI Certificate – Fundamentals of Deep Learning for Multi-GPUs, NVIDIA Deep Learning Institute, 2021.

• CEM Faculty Travel & Conference Fund, ≈750£, 2025.
• Divergence and Consensus in Explainable AI Feature Importance Ranking Methods for Solar Forecasting, CEM Faculty Research Fund, ≈300£, 2025.

Guest Editor, Special Issue "Intelligent Energy Forecasting Solutions: Machine Learning Driving Renewable Energy Advancements", Applied Sciences (IF = 2.7) , MDPI

https://www.mdpi.com/journal/applsci/special_issues/T5G3U361F1

- External Examiner for Abertay University.

-Independent Assessor for the Level 6 Data Scientist Degree Apprenticeship Programme at the University of Nottingham.

- External Academic Advisor for Birmingham City University.

- External Grant Reviewer, Dutch Research Council.

- Reviewer for various publishers such as Elsevier, Springer, MDPI, Hindawi, and IET.

- Technical Program Committee member for various international conferences worldwide, including those held in Spain, Finland, Norway, Malaysia, United Arab Emirates, Bahrain, and Egypt.

Prospective students with their own funding are encouraged to reach out. Please provide a brief overview of your research interests and academic background. I am particularly interested in the following areas:

• Forecasting Large Collections of Time Series Data
• Explainable AI for Time Series Forecasting
• Hierarchical Time Series Forecasting
• Graph Neural Networks for Time Series Forecasting

If you have a specific project or concept in mind that aligns with my research interests, please include it in your introduction. 

While I have a particular interest in time series forecasting and its various aspects, I am open to exploring other related topics within the field of machine learning.