MBZUAI has appointed Professor Mohsen Guizani as Associate Provost for Faculty Affairs and Institutional Advancement, bringing over 30 years of academic experience. Guizani previously served as the Founding Associate Vice President for Graduate Studies at Qatar University. He has secured over $30 million in research grants and published extensively in AI, IoT, and cybersecurity. Why it matters: This appointment strengthens MBZUAI's leadership and faculty development, contributing to its goal of becoming a globally recognized institution in AI research and education.
MBZUAI's Associate Provost Mohsen Guizani and his co-authors won the IEEE ComSoc - CSIM Best Journal Paper Award for 2021 for their paper "Reliable Federated Learning for Mobile Networks." The award will be presented at the IEEE International Communications Conference in Seoul. The paper's findings are expected to improve the reliability of federated learning tasks in mobile networks. Why it matters: The award recognizes impactful research in federated learning, an area of growing importance for distributed AI applications, and highlights MBZUAI's increasing prominence in the field.
MBZUAI Adjunct Professor Mérouane Debbah was named a Highly Cited Researcher by Clarivate, placing him in the top 1% of researchers globally. Debbah, who joined MBZUAI's machine learning department in 2021, also serves as chief researcher at the Technology Innovation Institute (TII) in Abu Dhabi. His research focuses on multi-agent reinforcement learning, distributed AI, and applying AI to improve telecommunications, including reducing dead spots and improving energy efficiency. Why it matters: This recognition highlights the UAE's growing prominence in AI research, particularly in leveraging AI for advancements in telecommunications infrastructure and multi-agent systems.
Professor Mérouane Debbah, Chief Researcher at AIDRC, and his co-authors received the 2022 IEEE TAOS TC Best GCSN Paper Award for their work on federated quantized neural networks. The paper, presented at IEEE ICC 2022, explores the tradeoff between energy, precision, and accuracy in these networks. The research proposes an optimal quantization level to minimize energy consumption during training, making it less prohibitive for mobile devices. Why it matters: The award recognizes work that reduces the carbon footprint of large-scale AI systems, a key challenge for sustainable AI deployment in the region and globally.
KAUST Professor Mohamed-Slim Alouini received the Organization of Islamic Cooperation (OIC) Science and Technology (S&T) Achievement Award at the First OIC Summit on Science and Technology. The award recognizes Alouini's contributions to science and technology within the OIC member states. Why it matters: Recognition at the OIC level highlights KAUST's impact and Professor Alouini's leadership in advancing science and technology across the Islamic world.
Dr. Zhiqiang Lin from Ohio State University presented the Security-Enhanced Radio Access Network (SE-RAN) project to address cellular network threats using O-RAN. The project includes 5G-Spector, a framework for detecting L3 protocol exploits via MobiFlow and MobieXpert, and 5G-XSec, a framework leveraging deep learning and LLMs for threat analysis at the network edge. Dr. Lin also outlined a vision for AI convergence with cellular security for enhanced threat detection. Why it matters: Enhancing 5G security through AI and open architectures is critical for protecting next-generation mobile networks in the GCC region and globally.
Fudan University's Zhongyu Wei presented research on social simulation driven by LLMs, covering individual and large-scale social movement simulation. Wei directs the Data Intelligence and Social Computing Lab (Fudan DISC) and has published extensively on multimodal large models and social computing. His work includes the Volcano multimodal model, DISC-MedLLM, and ElectionSim. Why it matters: Using LLMs for social simulation could provide new tools for understanding and potentially predicting social dynamics in the Arab world.
A recent talk at MBZUAI discussed "Green Learning" and Operational Neural Networks (ONNs) as efficient alternatives to CNNs. ONNs use "nodal" and "pool" operators and "generative neurons" to expand neuron learning capacity. Moncef Gabbouj from Tampere University presented Self-Organized ONNs (Self-ONNs) and their signal processing applications. Why it matters: Exploring more efficient AI models is crucial for sustainable development of AI in the region, as it addresses computational resource constraints and promotes broader accessibility.