Mustafa Albalushi

I am a PhD Researcher in Computer Science at the University of Exeter. My research focuses on artificial intelligence, machine learning, and deep learning approaches for time-series anomaly detection, with particular interest in decomposition-based methods, LSTM Autoencoders, and Transformer architectures for analysing univariate and multivariate time-series data.

My research explores the development of intelligent methods for detecting abnormal patterns in complex systems, combining data-driven modelling, deep learning techniques, and practical system knowledge. My broader research interests include artificial intelligence, cybersecurity, blockchain applications, secure Internet of Things (IoT) environments, and graph-based anomaly detection. I am also developing my knowledge in computer vision and image processing.

Alongside my research, I have experience teaching and supporting undergraduate and postgraduate students in areas including Python programming, computer networks, cybersecurity, and machine learning. My teaching approach focuses on connecting theoretical knowledge with practical applications and supporting students in developing industry-relevant computing skills.

Before starting my doctoral research, I gained over 20 years of professional experience in enterprise networking, cybersecurity, cloud infrastructure, and IT leadership within higher education and government sectors. I have led large-scale network and security operations, managed enterprise infrastructure, and delivered secure technology solutions across complex organisational environments.

I hold several professional certifications, including Cisco Certified Network Professional (CCNP), Cisco Certified Network Associate (CCNA), Microsoft certifications (MCSE, MCITP, MCSA), Certified Ethical Hacker (CEH), ITIL Foundation, CompTIA A+, and Project Management Professional (PMP).

My long-term goal is to combine artificial intelligence research with my background in networking and cybersecurity to develop intelligent, reliable, and practical solutions for real-world computing challenges.