Profiles

Dr. Sultan Albarakati is the Director of KAUST Academy and a key leader in STEM education in Saudi Arabia. In this role, he leads initiatives to provide world-class training and educational programs for Saudi students and professionals centered on upskilling the national workforce, particularly in artificial intelligence, machine learning and data science.

His work aims to support the Kingdom's rapidly transforming economy through partnerships with universities and industry while fostering talent development, contributing to National Talent Development in line with Vision 2030.

Dr. Albarakati earned his Ph.D. in 2020 and his M.S. in 2014, both in Applied Mathematics from KAUST, following a B.S. in Mathematics from Umm Al-Qura University in 2004. 

Before joining KAUST, he played a significant role in mentoring Saudi Arabia's Math Olympiad teams, leading them to notable international success.

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.

Yue Wang is a Ph.D. candidate at the Photonics Laboratory at King Abdullah University of Science and Technology (KAUST), under the mentorship of Prof. Boon S. Ooi. She earned her B.Eng. in Electronic Science and Technology from the University of Electronic Science and Technology of China (UESTC) in 2020. She received her master's degree in Electrical Engineering from KAUST in 2021.

Yue Wang's research focuses on semiconductor optoelectronics, high-speed color-converting luminescent devices, and optical wireless communication. She specializes in characterizing color-converting materials, designing and fabricating luminescent optoelectronic devices, and developing optical wireless communication systems.

Yue Wang has explored emerging luminescent materials such as perovskites, metal-organic frameworks (MOFs), and organic fluorophores, enabling Gb/s visible light communication and wavelength-division multiplexing with enhanced channel capacity. To relieve the stringent requirements of pointing, acquisition, and tracking in underwater optical wireless communication channels, she employed scintillating fibers and luminescent solar concentrators for wide field-of-view, high-speed photodetection. Her work also extends to optical amplification, where she has developed a high-gain visible-light amplifier based on perovskite quantum dots, potentially addressing the optical loss during long-distance optical data transmission.