Najwa Aaraj, Chief Researcher at the Cryptography Research Centre at TII, has joined MBZUAI as the first female faculty member in the Machine Learning Department. Aaraj leads R&D of cryptographic technologies, including post-quantum cryptography and lightweight cryptographic libraries. Her research will focus on the intersection of cryptography, cybersecurity, and machine learning, including using ML for cryptanalysis and protecting ML models with cryptography. Why it matters: This appointment strengthens MBZUAI's expertise in a critical area of AI security and cryptography, fostering cross-disciplinary research and innovation in the UAE.
Dr. Najwa Aaraj has been appointed as the new CEO of the Technology Innovation Institute (TII), succeeding Dr. Ray O. Johnson. Dr. Aaraj previously served as Chief Researcher at TII's Cryptography Research Center (CRC) and the Autonomous Robotics Research Center (ARRC). TII has 10 research centers specializing in fields like AI, robotics, and quantum technology and is known for the Falcon LLM series. Why it matters: This appointment signals a continued focus on R&D and innovation in Abu Dhabi, reinforcing the UAE's position as a global hub for advanced technology research.
Dr. Najwa Aaraj from MBZUAI and TII discussed the impact of quantum computers and machine learning on cryptographic algorithms. The talk covered post-quantum cryptographic (PQC) schemes, standardization efforts, and the role of machine learning in advancing cybersecurity solutions. Dr. Aaraj also highlighted the challenges of transitioning current cryptographic systems to quantum-resistant alternatives. Why it matters: As quantum computing advances, understanding and implementing post-quantum cryptography is crucial for maintaining secure communications and data protection in the UAE and globally.
Researchers including Dr. Najwa Aaraj developed ML-FEED, a new exploit detection framework using pattern-based techniques. The model is 70x faster than LSTMs and 75,000x faster than Transformers in exploit detection tasks, while also being slightly more accurate. The "ML-FEED" paper won best paper at the 2022 IEEE International Conference on Trust, Privacy and Security in Intelligent Systems and Applications. Why it matters: This research enables more efficient real-time security applications and highlights growing AI expertise in the Arab world.
KAUST professor Niveen Khashab was named the first Great Arab Mind in natural sciences. The Great Arab Minds award was conceived by Sheikh Mohammed Bin Rashid al Maktoum to recognize Arabs for achievements in science, architecture, engineering, economics, literature, and medicine. Khashab, an organic chemist, was selected for contributions to nanomaterials development, synthesis, and application in medicine, industry, and agriculture. Why it matters: The award highlights KAUST's role in fostering scientific talent and contributing to advancements in nanotechnology and related fields within the Arab world.
The ArabJobs dataset is a new corpus of over 8,500 Arabic job advertisements collected from Egypt, Jordan, Saudi Arabia, and the UAE. The dataset contains over 550,000 words and captures linguistic, regional, and socio-economic variation in the Arab labor market. It is available on GitHub and can be used for fairness-aware Arabic NLP and labor market research.
KAUST alumna Yasmeen Najm, a graduate of the KAUST Gifted Student Program (KGSP), is now a Strategic Partnerships and Engagement innovator at NEOM. She credits her interdisciplinary background and the opportunities provided by KGSP for her success. Najm was also a keynote speaker at the KGSP Pre-Departure Orientation, where she shared her experiences with 107 high-achieving Saudi STEM graduates. Why it matters: This highlights the impact of KAUST's KGSP in developing Saudi talent and contributing to the Kingdom's innovation goals, particularly in megaprojects like NEOM.
This paper presents a comprehensive evaluation of ChatGPT's performance across 44 Arabic NLP tasks using over 60 datasets. The study compares ChatGPT's capabilities in Modern Standard Arabic (MSA) and Dialectal Arabic (DA) against smaller, fine-tuned models. Results show ChatGPT is outperformed by smaller, fine-tuned models and exhibits limitations in handling Arabic dialects compared to MSA. Why it matters: The work highlights the need for further research and development of Arabic-specific NLP models to overcome the limitations of general-purpose models like ChatGPT.