EURECOM researchers developed data-driven verification methods using structured datasets to assess statistical and property claims. The approach translates text claims into SQL queries on relational databases for statistical claims. For property claims, they use knowledge graphs to verify claims and generate explanations. Why it matters: The methods aim to support fact-checkers by efficiently labeling claims with interpretable explanations, potentially combating misinformation in the region and beyond.
TII Chief Researcher Mérouane Debbah and MBZUAI President Eric Xing visited École Polytechnique in France to discuss AI research and training. They reviewed AI projects and opportunities to increase the visibility of UAE-led research. The meeting aimed to strengthen collaboration between MBZUAI, TII, and École Polytechnique. Why it matters: Such partnerships can foster knowledge exchange and accelerate AI innovation in the UAE by leveraging international expertise.
AIDRC researchers co-authored an accepted IEEE Vehicular Technology Magazine article on time reversal for 6G wireless communications. The article presents experimental results on the spatiotemporal focusing capability of time reversal across carrier frequencies. It examines requirements for efficient time reversal operation and synergies with technologies like reconfigurable intelligent surfaces. Why it matters: The research explores advancements in 6G wireless communication, with potential implications for coverage extension, sensing, and localization capabilities in the region.
A KAUST student blog post discusses optical wireless communications (OWC) as a solution to radio frequency exhaustion. OWC uses optical frequencies to carry electrical signals, offering advantages like high data rates and immunity to electromagnetic interference. Free-space optical (FSO) communication, a type of OWC, is applicable for inter-building connections and has seen use cases such as broadcasting during the 2010 FIFA World Cup. Why it matters: OWC research and deployment in the region can support high-bandwidth applications and provide cost-effective connectivity solutions, especially in challenging environments or disaster scenarios.
Prof. Mérouane Debbah, Chief Researcher at the AI Cross-Center Unit and DSRC, has been ranked No. 1 in France and 180th globally in Electronics and Electrical Engineering by Research.com. He has an H-index of 98 and over 47000 citations. Debbah was also recognized as a 2022 AI 2000 Most Influential Scholar in Internet of Things for contributions between 2012 and 2021. Why it matters: This recognition highlights the growing AI research talent within the GCC region, particularly in areas related to communication technologies and IoT.
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.
Researchers from the AI and Digital Science Research Center (AIDRC) won the Best Paper Award at the 2022 IEEE Global Communications Conference (GLOBECOM) for their paper "RSMA for Dual-Polarized Massive MIMO Networks: A SIC-Free Approach". The paper introduces a dual-polarized RSMA technique for downlink massive MIMO networks, using the polarization domain. Their approach relaxes the computational burden of successive interference cancellation and delivers high data rates. Why it matters: This award recognizes impactful research from the UAE on optimizing wireless communication using AI, which can contribute to advancements in 5G and beyond.
Technology Innovation Institute's (TII) Directed Energy Research Center (DERC) is integrating machine learning (ML) techniques into signal processing to accelerate research. One project used convolutional neural networks to predict COVID-19 pneumonia from chest x-rays with 97.5% accuracy. DERC researchers also demonstrated that ML-based signal and image processing can retrieve up to 68% of text information from electromagnetic emanations. Why it matters: This adoption of ML for signal processing at TII highlights the potential for advanced AI techniques to enhance research and security applications in the UAE.