DR CHAIMAE ASAAD 

DR CHAIMAE ASAAD

Assistant Professor, Computer Science Programme, School of Computing and Data Science

Academic Qualifications
  • 2025, PhD in Computer Science (Artificial Intelligence & NoSQL Data Systems), ENSIAS, Mohammed V University, Morocco, jointly supervised with International University of Rabat (UIR), Morocco.
  • 2018, MRes in Data Science and Big Data, High National School of Computer Science and Systems’ Analysis (ENSIAS), Mohammed V University, Morocco
  • 2016, BSc in Computer Science, Faculty of Sciences, Mohammed V University, Morocco
Professional Experience
  • Dr. Chaimae Asaad is an Assistant Professor at Oryx University in Qatar, in partnership with Liverpool John Moores University, with over eight years of experience in research and development across information systems, data science, and artificial intelligence. Her expertise includes intelligent NoSQL database modelling and querying, machine learning, natural language processing, large language models, knowledge graphs, and agentic AI, with interdisciplinary applications spanning bioinformatics, precision medicine, environmental modelling, and epidemiological research. She has a diversified research profile combined with industry experience, having served as a Senior Research Fellow, R&D project management lead, data science consultant for both public and private sectors, and advisor to tech startups on AI adoption and investment.
  • During her tenure at the ICT Laboratory at the International University of Rabat – a member of the International Associated Laboratory LIA DATANET – she contributed to internationally funded projects and grant proposals (including VLIR-UOS, MaScIR, and CNRST), participated in patent development, and actively disseminated research through peer-reviewed publications and international conferences. She collaborated with leading institutions such as KU Leuven, UM6P, CHU Rabat, and CNRST, as well as international research communities focused on large-scale data systems and AI-driven analytics. She is a professional member of ACM and SDL Forum Society, a reviewer for leading journals, and provides AI training and advisory services for diverse audiences.
  • She has a rich higher education teaching experience across undergraduate and postgraduate programmes, delivering advanced technical courses in Machine Learning, AI fundamentals, Database Management Systems, NoSQL databases, Hadoop infrastructure, and CS Research Methodology. She has also delivered AI-oriented courses tailored for non-technical audiences, including Contract Law in the Digital Age and Data Visualisation for Economics. In addition, she has supervised undergraduate and postgraduate dissertations and projects, providing research guidance and technical training to support student research and academic development.
Research Interests
  • Dr. Asaad’s research focuses on advancing artificial intelligence and data science within information systems, with particular emphasis on intelligent NoSQL database modelling, querying, optimization, and heterogeneous data quality assessment. She explores emerging paradigms such as large language models, agentic AI, and knowledge graphs, alongside participatory modelling environments that support collaborative and data-driven decision-making.
  • Her applied research spans areas including digital personalized health, environmental data analytics, and epidemic bio-surveillance using AI and social media, with a strong emphasis on ethical, responsible, and human-centered AI. She has been actively engaged in internationally funded research initiatives and grant development, master’s students supervision, and contribution to innovation through grant proposals, patent development and scientific dissemination in peer-reviewed venues. She has collaborated with academic and industry partners, including KU Leuven, Oracle, UM6P, CHU, and CNRST, on interdisciplinary projects addressing scalable data systems and AI-enabled decision support.
Selected Publications
  • Asaad et al. “When Infodemic Meets Epidemic: A Systematic Literature Review”. JMIR Public Health and Surveillance Journal. 2025.
  • Asaad et al. “Bridging the Gap: Participatory Modeling for Stakeholder-Driven NoSQL Database Design”. ACM/IEEE 27th International Conference on Model Driven Engineering Languages and Systems Companion. Austria. 2024.
  • Gryech, Asaad et al. “Applications of machine learning & Internet of Things for outdoor air pollution monitoring and prediction: A systematic literature review”. Engineering Applications of Artificial Intelligence. 2024.
  • Asaad et al. “Towards Leveraging Artificial Intelligence for NoSQL Data Modeling, Querying and Quality Characterization.” ACM/IEEE MODELS-C, DocSym. IEEE, 2023. Sweden.
  • Asaad et al. “Investigating the Perceived Usability of Entity-Relationship Quality Frameworks for NoSQL Databases.” MEDI Conference 2023. Springer Nature.