KAUST and Gulf Data Hub, along with Data Hub Tech and Ashi Bushnag Co., held a groundbreaking ceremony on September 18 to celebrate the development of a new data center on the KAUST campus. The data center will be built by Gulf Data Hub in partnership with the Saudi company Data Hub Tech and Jeddah-based design and contracting firm Ashi Bushnag Co. KAUST Interim President Nadhmi Al-Nasr and Gulf Data Hub CEO Tarek Al-Ashram exchanged gifts during the ceremony. Why it matters: This data center project signifies growing investment in Saudi Arabia's technological infrastructure and KAUST's role as a hub for innovation.
This paper introduces a deep vision-based framework for predicting coastal floods under climate change, addressing the challenges of limited training data and high-dimensional output. The framework employs and compares various deep learning models, including a custom compact CNN architecture, against geostatistical and traditional machine learning methods. A new synthetic dataset of flood inundation maps for Abu Dhabi's coast is also provided to benchmark future models.
This paper introduces an AI-driven decision support system for green hydrogen investment in Oman, specifically for the Duqm R3 auction. The system uses publicly available meteorological data to predict maintenance pressure on hydrogen infrastructure, creating a Maintenance Pressure Index (MPI). This tool supports regulatory oversight and operational decision-making by enabling temporal benchmarking against performance claims.
KAUST researchers studied the meteorological origins of sea-level extremes in the Red Sea using computer simulations and the ADCIRC storm surge model. They validated their datasets with hourly sea-level observations from six tidal gauges along the Saudi coast. The study found that wind variations over the southern part of the sea are the main drivers of basin-wide sea-level extremes. Why it matters: This research provides critical insights for managing and developing the Red Sea coastline, including megacity projects and tourism, while mitigating their impact on the marine environment.
This paper focuses on analyzing surveys of women entrepreneurs in the UAE using machine learning techniques. The goal is to extract relevant insights from the data to understand the current landscape and predict future trends. The study aims to support better business decisions related to women in entrepreneurship.