معرفی
Nikolaos Polatidis is a Principal Lecturer in Computer Science at the University of Brighton, within the School of Architecture, Technology and Engineering. He has been a continuous academic staff member since 2017, progressing from Research Fellow to Principal Lecturer by 2022. He holds a PhD in Applied Informatics from the University of Macedonia, an MSc in Internet Software Systems from the University of Birmingham, and a BSc in Computer Science from Heriot-Watt University.
- BSc, Computer Science, Heriot-Watt University, 2008
- MSc, Internet Software Systems, University of Birmingham, 2011
- PhD, Applied Informatics, University of Macedonia, 2017
His research centers on machine learning and cybersecurity, with a strong focus on practical applications such as Android malware detection, automated machine learning (AutoML), and ethical AI. He explores how large language models (LLMs) can be used for code generation in security contexts and how ethical narratives can improve the trustworthiness of AI-generated models. His work often integrates deep learning, natural language processing, and privacy-preserving techniques in mobile and IoT environments.
The recent publication trends show a strong emphasis on the intersection of AI and cybersecurity, particularly leveraging LLMs and AutoML for malware detection and ethical model generation. Many of his recent papers involve collaborative filtering, recommender systems, and deep learning architectures like convolutional neural networks. There is a clear trajectory toward intelligent, ethical, and automated AI systems in real-world applications.
He has received recognition as a Fellow of the Higher Education Academy (FHEA) and serves on the editorial boards of several journals, including The Computer Journal, IET Networks, and Applied Artificial Intelligence.
Nikolaos actively supervises postgraduate research in machine learning and cybersecurity. He has led curriculum development by integrating research into teaching, such as introducing Machine Learning for Cybersecurity modules and guiding student projects that result in peer-reviewed publications. He is the Principal Investigator (PI) of the ALFIE project (Assessment of Learning technologies and Frameworks for Intelligent and Ethical AI), funded from 2024 to 2027, which explores ethical and intelligent learning technologies. His grant work emphasizes AutoML, learning frameworks, and ethical AI in education.
He is involved in several research teams and collaborates internationally, particularly in AI for cybersecurity and educational technologies. His lab activities focus on developing and testing machine learning models for Android malware detection, ethical AI frameworks, and recommender systems. The ALFIE project indicates an active research group working at the intersection of AI, ethics, and learning technologies.
