KAUST Discovery Ph.D. student Chun-Ho Lin received the best paper award at the 2nd International Symposium on Devices and Application of Two-dimensional Materials in June 2016. The award recognizes Lin's contributions to the field of two-dimensional materials. Why it matters: Recognition of KAUST student research highlights the university's contributions to advanced materials science.
MBZUAI Professor Chih-Jen Lin gave a keynote at the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval in Taipei. Lin's address, titled ‘On the “Rough Use” of Machine Learning Techniques’, focused on instances where machine learning techniques are employed inappropriately, using examples from graph representation learning and deep neural networks. He advocated for the development of high-quality, user-friendly software to improve the practical application of machine learning and mitigate misuse. Why it matters: Showcases MBZUAI's faculty expertise and contributions to the discussion on responsible AI research and deployment on a global stage.
The Secure Systems Research Center (SSRC) won the Best Paper Award at EWSN 2023 for "BLoB: Beating-based Localization for Single-antenna BLE Devices," which introduces a method using concurrent transmissions to localize Bluetooth tags accurately. The system achieves sub-meter accuracy in indoor environments by having multiple anchors transmit simultaneously. A second SSRC paper, "InSight: Enabling NLOS Classification...", was also a runner-up in the Best Paper category. Why it matters: This award highlights the growing research capabilities in IoT and localization technologies within the GCC region, particularly for indoor environments where GPS is unavailable.
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 Ph.D. student Jinhui Xiong won the best paper award at the 24th International Symposium on Vision, Modeling, and Visualization in Germany for his paper "Stochastic Convolutional Sparse Coding". The paper, co-authored with KAUST Professors Peter Richtárik and Wolfgang Heidrich, introduces a novel stochastic spatial-domain solver for Convolutional Sparse Coding (CSC). The proposed algorithm outperforms state-of-the-art solutions in terms of execution time and offers an improved representation for learning dictionaries from sample images. Why it matters: This award recognizes significant research in efficient image representation and dictionary learning, contributing to advancements in visual computing and AI at KAUST.