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.
Areej Aljarb is a Ph.D. student in material science and engineering at KAUST, researching 2D materials within the KAUST 2D Materials Research Lab under Professors Lain-Jong Li and Xixiang Zhang. Her research focuses on the controlled growth and fundamental phenomena of two-dimensional atomic layer thin materials, specifically controlling the orientation of 2D transition metal dichalcogenides (TMDs). Aljarb aims to achieve single-orientation epitaxial monolayer 2D TMDs to fully utilize the potential of these materials. Why it matters: This highlights KAUST's commitment to fostering local talent and contributing to advanced materials research with potential applications in various technology sectors.
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 and Saudi Aramco have partnered to establish the Saudi Arabia Advanced Research Alliance (SAARA) and Technovia, a company focused on commercializing R&D in Saudi Arabia. SAARA includes KACST, KFUPM, TAQNIA, and RTI International, aiming to translate technology into commercially viable products. Technovia, located in Dhahran Techno Valley, will prepare technologies for market entry and secure external investment. Why it matters: This initiative aims to accelerate technology development and economic diversification in Saudi Arabia by bridging the gap between research and commercial applications, potentially fostering innovation across various industrial sectors.
The paper introduces AraGPT2, a suite of pre-trained transformer models for Arabic language generation, with the largest model (AraGPT2-mega) containing 1.46 billion parameters. Trained on a large Arabic corpus of internet text and news, AraGPT2-mega demonstrates strong performance in synthetic news generation and zero-shot question answering. To address the risk of misuse, the authors also released a discriminator model with 98% accuracy in detecting AI-generated text. Why it matters: This release of both the model and discriminator fills a critical gap in Arabic NLP and encourages further research and applications in the field.