Baidaa Al-Bander

Biography

Baidaa completed her MSc in Computer Engineering and pursued her PhD in Electrical and Computer Engineering at the University of Liverpool, with a focus on advanced signal processing (images) and AI techniques. Following her doctorate, Baidaa held a Postdoctoral Research position at the University of Dundee, where she expanded her expertise in applied AI and healthcare technology. In 2022, she joined the School of Computing and Mathematics at Keele University, contributing to research and teaching in areas including artificial intelligence, ethical and safe AI, and interdisciplinary applications of computing.

Research and scholarship

Baidaa’s research centres on the ethical development and trustworthy deployment of Artificial Intelligence (AI) technologies, with a strong emphasis on healthcare applications. Her work spans the full research spectrum—from the design and theoretical analysis of AI algorithms to their practical implementation and rigorous experimental evaluation on real-world medical and other domain-specific datasets. A key pillar of her research is Ethical AI, where she prioritises transparency, interpretability, and safety, aiming to create AI models that not only deliver accurate predictions but also offer clear, reliable, and understandable results to stakeholders. She is engaged in identifying and addressing bias within machine learning systems, including large language models (LLMs), to promote fairness and equity in decision-making processes. Baidaa’s interdisciplinary collaborations with experts have enriched her perspective, enabling her to tailor AI solutions that meet the complex needs of various fields and inspire new research avenues. Her scholarship reflects a passion for bridging cutting-edge AI methods with practical challenges, driving innovations in explainable AI, computer vision, and pattern recognition. She regularly serves as a reviewer for prestigious journals and international venues. Baidaa is committed to building and leading a research team dedicated to harnessing AI’s potential for real-world impact.

Research interests:

• AI in Healthcare
• AI in Engineering
• Explainable and Trustworthy AI
• Ethical and Safe AI
• Agentic AI
• Multimodal Data Integration and Real-World Data Analytics
• Computer Vision and Pattern Recognition
• Deep Learning Architectures and Applications