Dr. Mohamed Ferrag, a Lead Researcher at AIDRC, received the "Algeria Scopus Award" from Algeria's Ministry of Education and Higher Research for his contributions to computer science. Dr. Ferrag is an IEEE Senior Member and has authored many scientific papers and books. He was also ranked among the World's Top 2% Scientists from Stanford University in 2020-2022. Why it matters: This award recognizes and encourages scientific research and talent within the Algerian AI and computer science community.
KAUST Professor Mohamed Eddaoudi has won the 2023 Kuwait Prize in chemistry for his work on functional solid-state materials, specifically metal-organic frameworks (MOFs). His research focuses on innovative design strategies for these materials and their applications in gas separations, catalysis, energy storage, and carbon capture. Eddaoudi, a founding faculty member at KAUST since 2009, shares the prize with Prof. Nashaat Nassar from the University of Calgary. Why it matters: The award recognizes KAUST's research excellence and highlights the importance of materials science for energy and environmental sustainability within the Arab world.
MBZUAI alumnus Ahmed Sharshar is developing smaller AI models to make the technology more accessible, especially in resource-constrained environments like Egypt. His master's thesis involved creating an app that assesses lung health using mobile phone video analysis, eliminating the need for traditional medical devices. Sharshar is pursuing his Ph.D. at MBZUAI, focusing on lightweight and energy-efficient models for various applications. Why it matters: Democratizing AI through smaller, efficient models can enable broader applications and innovation across diverse sectors in the Middle East and beyond.
KAUST Ph.D. student Mohammed Aljahdali received the Best Paper award at the International Conference on Federated Learning Technologies and Applications (FLTA) 2025 for his research on federated learning. His paper, "Flashback: Understanding and Mitigating Forgetting in Federated Learning," introduces an algorithm to help AI systems retain knowledge across diverse datasets while preserving privacy. Aljahdali's research, supervised by Professor Marco Canini, focuses on training machine learning models directly on user devices. Why it matters: This award recognizes the growing talent and impactful research emerging from Saudi universities in the field of privacy-preserving AI.
KAUST Professor Mohamed Eddaoudi is researching MOFs (metal-organic frameworks). MOFs have applications for clean energy. Why it matters: This research contributes to KAUST's and Saudi Arabia's broader clean energy and sustainability initiatives.
MBZUAI graduate Ahmed Sharshar developed a computer vision application that assesses lung health from a video of a person breathing, estimating Forced Vital Capacity (FVC), Forced Expiratory Volume in 1 second (FEV1), and Peak Expiratory Flow (PEF). The model achieved up to 100% accuracy using thermal video data from 60 participants. Sharshar aims to create lightweight models applicable in developing countries without high-end GPUs. Why it matters: This research showcases the potential of AI to democratize healthcare access through non-invasive, accessible diagnostic tools.
KAUST research scientist Dr. Maged Saad is working on unconventional methods for global food security within the Desert Agriculture Initiative. His research involves using selected strains of bacteria to increase salt tolerance and crop productivity in desert plants. Dr. Saad aims to convert this technology into a marketable product by securing intellectual property rights, testing prototypes with Saudi farmers, and establishing a startup. Why it matters: This research aligns with Saudi Arabia's Vision 2030 goals to enhance local agricultural production and promote sustainable solutions for food security in arid environments.
The authors introduce Nile-Chat, a collection of LLMs (4B, 3x4B-A6B, and 12B) specifically for the Egyptian dialect, capable of understanding and generating text in both Arabic and Latin scripts. A novel language adaptation approach using the Branch-Train-MiX strategy is used to merge script-specialized experts into a single MoE model. Nile-Chat models outperform multilingual and Arabic LLMs like LLaMa, Jais, and ALLaM on newly introduced Egyptian benchmarks, with the 12B model achieving a 14.4% performance gain over Qwen2.5-14B-Instruct on Latin-script benchmarks; all resources are publicly available. Why it matters: This work addresses the overlooked aspect of adapting LLMs to dual-script languages, providing a methodology for creating more inclusive and representative language models in the Arabic-speaking world.