KAUST researchers developed a machine learning algorithm to control a deformable mirror within the Subaru Telescope's exoplanet imaging camera, compensating for atmospheric turbulence. The algorithm, which computes a partial singular value decomposition (SVD), outperforms a standard SVD by a factor of four. The KAUST team received a best paper award at the PASC Conference for this work, which has already been deployed at the Subaru Telescope. Why it matters: This advancement enables sharper images of exoplanets, facilitating their identification and study, and showcases the impact of optimizing core linear algebra algorithms.
MBZUAI researchers developed AirCast, a novel AI model for improved air pollution forecasting, which won the best paper award at the TerraBytes workshop during ICML. AirCast fuses weather and chemistry data using a Vision Transformer and frequency-weighted MAE to better predict extreme events like Saharan dust storms. In tests across the Middle East and North Africa, AirCast reduced PM2.5 error by 33% compared to a persistence baseline and outperformed the CAMS physics model. Why it matters: Accurate air pollution forecasting is critical for public health in the GCC region, and this research demonstrates a significant advancement using AI to address this challenge.
MBZUAI researchers developed a method to adapt Meta's Segment Anything Model (SAM) for medical image segmentation, addressing its performance gap with natural images. Their approach improves SAM's accuracy without requiring extensive retraining or large medical image datasets. The research, led by Chao Qin, was nominated for the Best Paper Award at the MICCAI conference in Marrakesh. Why it matters: This offers a more efficient and effective way to leverage foundation models in specialized medical imaging applications, potentially improving diagnostic accuracy and reducing the need for large-scale, domain-specific training data.
An MBZUAI team won the best paper award at the inaugural Arabic Natural Language Processing Conference for their work on processing Arabic speech. Their study establishes a new approach to tackle the complexities of spoken Arabic, which differs significantly from text-based language models. The team's approach aims to advance new tools for Arabic speakers by addressing challenges like intonation and the continuous nature of speech. Why it matters: This award highlights the importance of specialized research in Arabic NLP, as mainstream LLMs often face limitations in accurately processing the nuances of Arabic speech.
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.
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.
Cryptography Research Center's Prof. Francisco Rodriguez-Henriquez and PhD candidate Jorge Chavez-Saab won a Best Paper Award ahead of Asiacrypt 2022. Their paper, "SwiftEC: Shallue-van de Woestijne Indifferentiable Function to Elliptic Curves," was written in collaboration with Mehdi Tibouchi of NTT. The paper presents an improved variation of the Elligator Squared technique for representing points of arbitrary elliptic curves as close-to-uniform random strings. Why it matters: The award recognizes important cryptographic research from the UAE, contributing to the advancement of secure digital solutions.
KAUST Ph.D. student Mohammed Aljahdali received the Best Paper award at the International Conference on Federated Learning Technologies and Applications (FLTA) 2025 for his research on federated learning. His paper, "Flashback: Understanding and Mitigating Forgetting in Federated Learning," introduces an algorithm to help AI systems retain knowledge across diverse datasets while preserving privacy. Aljahdali's research, supervised by Professor Marco Canini, focuses on training machine learning models directly on user devices. Why it matters: This award recognizes the growing talent and impactful research emerging from Saudi universities in the field of privacy-preserving AI.