Profiles

Sakhaa Alsaedi is a Ph.D. graduate in Computer Science at King Abdullah University of Science and Technology (KAUST), under the supervision of Prof. Xin Gao and Prof. Takashi Gojobori. She received her bachelor’s degree in Computer Science from Taibah University in 2018 and her master’s degree in Computer Science from KAUST in 2020. She is the founder of the Medvation startup company, inventing educational kits that teach children concepts of robotics and Machine Learning (ML) in fun ways. She worked as a product developer at the Namma Al-Munawara company, Madinah.

Sakhaa's research focuses on developing principled computational frameworks for causal reasoning in medical digital twin systems by integrating multimodal biomedical data. More broadly, her work includes multi-omics data integration, causal biomedical knowledge graph construction, causal reasoning framework development, and genetic risk factor analysis. She aims to develop computational approaches that advance the understanding of complex biological systems and genetic risk factors, ultimately supporting precision medicine and broader data-driven discoveries in biomedicine.

Sumyyah Toonsi is a PhD candidate in Computer Science at King Abdullah University of Science and Technology (KAUST), with a focus on bioinformatics. Her work integrates text mining, genetic risk prediction, and causal inference to advance understanding of complex biomedical data. She applies computational methods to support data-driven discoveries in health and disease.

Xiang Chen is a Ph.D. candidate in Statistics in the Geospatial Statistics and Health Surveillance (GeoHealth) Group at King Abdullah University of Science and Technology (KAUST), supervised by Prof. Paula Moraga.

He received his B.Eng. and M.S. degrees in Computer Science from Harbin Institute of Technology (HIT), where he worked on automated machine learning and data-driven modeling methods. During his doctoral studies, he has developed advanced statistical and deep learning frameworks for dengue forecasting across Brazil, incorporating climate variability, spatial dependence, and mobility patterns.

His work has been published in journals including BMC Public Health, Tropical Medicine and Health, and Infectious Disease Modelling. His research aims to bridge statistical methodology and real-world health surveillance to support early warning systems and data-driven policy planning.

Xiang Chen's research focuses on the intersection of statistics, machine learning, and public health, with a primary emphasis on the spatio-temporal modeling of infectious diseases. His work leverages geospatial statistics, time series analysis, and human mobility modeling to understand disease spread and improve epidemiological forecasting. Additionally, he explores climate-health interactions within environmental epidemiology. To ensure that his predictive models - including deep learning approaches for public health data - remain transparent and actionable, he actively incorporates Explainable AI (XAI) and interpretable machine learning into his methodology.