Omran Ayoub

Models can achieve robust performance across diverse optical network configurations with a novel joint learning approach that enhances adaptability and efficiency.

Explanation-based runtime verification intercepts erroneous ML decisions in optical networks, preserving automation while enhancing reliability.

A medium-sized LLM outperforms larger models in delivering high-quality, efficient explanations for optical network automation tasks.

Human-understandable explanations for AI decisions can be achieved by augmenting traditional XAI methods with LLMs and mutual feature interactions, leading to significantly improved interpretability.