Registered user since Wed 24 Sep 2025
Shahd Seddik is a PhD student in Computer Science at the University of British Columbia. Her research focuses on AI for Software Engineering, with an emphasis on large language models for system observability, automation, and code representation techniques. She holds a Master’s degree in Data Science and Machine Learning from Queen’s University, where her thesis explored retrieval-augmented multi-hop question answering with knowledge graphs, and a BSc in Communications and Information Engineering from Zewail City, graduating first in her program with summa cum laude honors.
Prior to her PhD, Shahd worked as an Applied Scientist at Microsoft, where she designed and deployed large-scale ML and NLP systems across Bing Ads and Azure Cognitive Services. Her projects included LLM-based agentic workflows for observability, anomaly detection, and sentiment analysis, translating research into production with measurable business impact.
Her research and industry experience span agentic workflows, prompt engineering, anomaly detection, information retrieval, and applied ML for speech and text. She is particularly interested in bridging cutting-edge ML research with practical, scalable applications in software engineering.
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