I’m a Master’s by Research student in Computer Science at the University of Hull with over 12 years of experience in data-centric systems.
My academic and industry-driven work focuses on explainability, generative AI, and federated learning, with applications in multimodal and real-time AI systems.
Currently contributing to UK’s National Edge AI Hub and involved in projects that combine LLMs, RAG pipelines, and agentic AI programming for practical deployments in healthcare and smart infrastructure.
- Master’s by Research in Computer Science - University of Hull
- Bachelor’s in Statistics - Shiraz Azad University
- Explainability in deep learning models (LLMs, vision-language systems)
- Retrieval-Augmented Generation (RAG) and LLM evaluation
- Federated & edge learning systems
- Open-ended discovery and continual learning
-Explainability of Generative Models Research on interpretability techniques for large language models (LLMs), instruction-based image editing, and vision-language generation tasks.
-Smart Calendar Assistant Built an agentic AI system using LLMs, Whisper for speech-to-text, and RAG pipelines to automate event tracking and industrial knowledge retention.
-Federated Learning for Edge AI Developing privacy-preserving learning systems across distributed healthcare nodes, with a focus on explainability and real-time inference.
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Email: z.dehghani@hull.ac.uk
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