Dr. Shayma Alkobaisi

Title of the research page: Predictive Analytics

A significant part of her work centers on predictive analytics, where she applies statistical learning and data mining techniques to model and forecast human and environmental behavior. She has developed frameworks for analyzing mobility patterns, modeling uncertainty in moving-object databases, and predicting outcomes in health and environmental systems. Her research uses spatial-temporal data integration and sensor-based modeling to anticipate events such as changes in air quality or potential health risks, with applications ranging from smart cities to personalized healthcare. Notably, her ICT-funded project, “Health Monitoring System for Modeling Individual Exposure to Environmental Triggers of Asthma Episodes in the UAE,” exemplifies this approach—leveraging predictive analytics to connect environmental conditions with individual health responses and guide proactive health management strategies.

Dr. Alkobaisi has published extensively in leading international journals and conference proceedings in computer science, information systems, and health informatics. Her scholarly contributions include highly cited works on uncertainty management in spatial databases and predictive modeling for real-world applications. She has been the principal investigator and collaborator on several competitive national and institutional research grants, including projects funded by the ICT Fund, UAEU Research Office, and Emirates Foundation, supporting initiatives in health data analytics, smart environments, and AI-driven data systems. Her sustained record of funding and publication underscores her leadership in advancing research that connects technological innovation with societal impact.