Wafaa El Husseini

Leading multiple R&D initiatives in NLP, AI, and Data Science, with a strong focus on large language models and knowledge engineering. Designed and deployed ontologies and semantic data models to organize unstructured information for advanced analytics. Built Retrieval-Augmented Generation (RAG) pipelines combining LLMs with Knowledge Graphs to improve document understanding and semantic search. Applied fine-tuning and prompt engineering for domain-specific reasoning. Managed cross-functional Agile teams (≈15 members), providing mentorship, defining project roadmaps, and ensuring timely delivery. Authored technical documentation, whitepapers, and internal research reports to share best practices across teams.

Research project focusing on ontology mediated query answering. Results were published in the top international conferences for Semantic Web.

Taught various classes including:

• Object-Oriented Programming in Java

• Big Data Mining and Analysis: Hadoop (HDFS/Spark/Hive)

• Relational Databases

• Data Structures

• Created OntoGREPAR, an ontology that extends the work done in SIDES 3.0 for the medical field, to cover postgraduate educational training in medicine, focusing on the area of Anesthesia and Resuscitation.

• Extended Google's Blockly to develop an ontology-based, blocks-based user-friendly query interface, called "OntoBlockly" which allows end-users to conduct SPARQL queries without prior knowledge of the ontology or of the query language.

data extraction using SPARQL from a large data repository and treated them for on-demand information.