Shaheen Fatima
Professor
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Positions (1)
Chengyu Zhang
Loughborough University
3 months ago
Fully funded PhD opportunity at Loughborough University in Computer Science , focused on AI Safety and the detection/mitigation of security vulnerabilities in AI systems that use the Model Context Protocol (MCP) . The project is sponsored by Darktrace and includes a 25% stipend top-up above the standard rate. It is a UK/home students only studentship, and the university advertises it as a fully funded PhD with UK fees covered . Research themes include program analysis , formal verification , software testing , cybersecurity , and multi-agent systems . The project aims to identify potentially malicious MCP servers and develop defence mechanisms to protect AI agents from compromise. This is a strong fit for candidates interested in software engineering, programming languages, AI security, and trustworthy AI. Supervision is by Dr Chengyu Zhang (primary supervisor), with Dr Shaheen Fatima and Professor Georgina Cosma as secondary supervisors, all at Loughborough University. Entry requirements: a first-class undergraduate degree is required; if you also hold a master's degree, it must be at distinction . The advert highlights programming ability (e.g. Python, C/C++), and experience in machine learning, cybersecurity, or program analysis is an advantage. Strong analytical ability, communication skills, self-motivation, and independent research aptitude are expected. Deadline: 21 July 2026. Start date: October 2026. Apply online via the university page and contact the supervisor with your CV for pre-application details.
3 months ago
Articles (1)
The traditional matrix factorisation (MF)-based recommender system methods, despite their success in making the recommendation, lack explainable recommendations as the produced latent features are meaningless and cannot explain the recommendation. This article introduces an MF-based explainable recommender system framework that utilises the user-item rating data and the available item information to model meaningful user and item latent features. These features are exploited to enhance the rating prediction accuracy and the recommendation explainability. Our proposed feature-based explainable recommender system framework utilises these meaningful user and item latent features to explain the recommendation without relying on private or outer data. The recommendations are explained to the user using text message and bar chart. Our proposed model has been evaluated in terms of the rating prediction accuracy and the reasonableness of the explanation using six real-world benchmark datasets for movies, books, video games, and fashion recommendation systems. The results show that the proposed model can produce accurate explainable recommendations.
Year:
2022
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