Research output per year
Research output per year
Personal profile
Research interests
I am an innovative AI researcher focused on enhancing the accuracy and reliability of code generation in large language models. My work is dedicated to addressing the limitations of current Large Language Models (LLMs) in generating precise, efficient machine learning code. Using AutoML tools, I automate model optimisation and error correction, aiming for seamless code generation processes. I am also committed to embedding ethical AI frameworks, ensuring that advancements in NLP and code generation align with transparency, fairness, and safety in AI deployment.
Education/Academic qualification
Bachelor, BSc Computer Science & Artificial Intelligence, University of Brighton
Award Date: 10 Jul 2022
Keywords
- QA76 Computer software
- Machine Learning
- Automated Machine Learning
- Deep Learning
- Classification (Machine Learning)
- Computer Science
- Artificial Intelligence
- Neural Networks
- Chatbots
- Large Language Models
- Natural Language Processing
- Ethical AI
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Collaborations and top research areas from the last five years
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Enhancing LLM code generation using natural language processing in the contexts of machine learning
Nelson, J., Pavlidis, M., Fish, A. & Polatidis, N., 16 Mar 2025, Database Engineered Applications - 28th International Symposium, IDEAS 2024, Proceedings: 28th International Symposium, IDEAS 2024, Bayonne, France, August 26–29, 2024, Proceedings. Chbeir, R., Ilarri, S., Manolopoulos, Y., Revesz, P. Z., Bernardino, J. & Leung, C. K. (eds.). 1 ed. Springer, p. 92-105 14 p. (Lecture Notes in Computer Science; vol. 15511 LNCS).Research output: Chapter in Book/Conference proceeding with ISSN or ISBN › Conference contribution with ISSN or ISBN › peer-review
Open Access -
ChatGPT-Driven Machine Learning Code Generation for Android Malware Detection
Nelson, J., Pavlidis, M., Fish, A., Kapetanakis, S. & Polatidis, N., 8 Nov 2024, In: The Computer Journal. 60 p., bxae114.Research output: Contribution to journal › Article › peer-review