KAUST Ph.D. alumna Sabrina Vettori and Ph.D. student Yuxiao Li received a Distinguished Student Paper Award at the 2018 Eastern North American Region (ENAR) Spring Meeting of the International Biometric Society. Li's paper focused on efficient estimation for non-stationary spatial covariance functions, while Vettori's paper addressed Bayesian hierarchical modelling of air pollution extremes. Both students were recognized for their contributions to statistical environmental studies and air pollution modeling. Why it matters: This award highlights KAUST's commitment to fostering high-quality research and recognizes the achievements of its students in addressing critical environmental challenges.
This paper introduces neural Bayes estimators for censored peaks-over-threshold models, enhancing computational efficiency in spatial extremal dependence modeling. The method uses data augmentation to encode censoring information in the neural network input, challenging traditional likelihood-based approaches. The estimators were applied to assess extreme particulate matter concentrations over Saudi Arabia, demonstrating efficacy in high-dimensional models. Why it matters: The research offers a computationally efficient alternative for environmental modeling and risk assessment in the region.
Hamad Bin Khalifa University's Qatar Computing Research Institute (QCRI) introduced Fanar, an Arabic-centric multimodal generative AI platform featuring the Fanar Star (7B) and Fanar Prime (9B) Arabic LLMs. These models were trained on nearly 1 trillion tokens and are designed to address different prompts through a custom orchestrator. Fanar includes a customized Islamic RAG system, a Recency RAG, bilingual speech recognition, and an attribution service for content verification, sponsored by Qatar's Ministry of Communications and Information Technology. Why it matters: The platform signifies a major step towards sovereign AI development in Qatar, providing advanced Arabic language capabilities and addressing regional needs.
A new framework for constructing confidence sets for causal orderings within structural equation models (SEMs) is presented. It leverages a residual bootstrap procedure to test the goodness-of-fit of causal orderings, quantifying uncertainty in causal discovery. The method is computationally efficient and suitable for medium-sized problems while maintaining theoretical guarantees as the number of variables increases. Why it matters: This offers a new dimension of uncertainty quantification that enhances the robustness and reliability of causal inference in complex systems, but there is no indication of connection to the Middle East.
KAUST postdoctoral fellow Carolina Euán received the Sylvia Esterby Presentation Award from the International Environmentrics Society (TIES) for her talk on a spatio-temporal model applied to drought data in Mexico. The research, conducted with KAUST Associate Professor Ying Sun, focuses on modeling dependence between processes observed in two categories, such as dry or rainy days. Euán joined KAUST in 2016 after completing her Ph.D. in statistics from the Research Center in Mathematics (CIMAT), Guanajuato, Mexico. Why it matters: This award recognizes the quality of environmental statistics research being conducted at KAUST and its applicability to understanding complex environmental phenomena in the region and beyond.
Enowa and KAUST held the Enowa-KAUST Energy Summit 2024, celebrating the third year of their Energy Cortex Program. The Energy Cortex Program funds applied research for clean energy solutions, focusing on renewable energy technologies led by KAUST faculty. The program is structured around Weatherlytics, GenFlex Cortex, Stor Cortex, and Grid Cortex, and has engaged KAUST professors, produced six journal papers, and provided NEOM with data. Why it matters: This partnership aims to revolutionize renewable energy in Saudi Arabia by integrating AI and advanced data analytics to optimize energy generation and distribution, supporting the Kingdom's sustainable energy goals.
MBZUAI researchers, in collaboration with Monash University, have introduced ArEnAV, a new dataset for deepfake detection featuring Arabic-English code-switching. The dataset comprises 765 hours of manipulated YouTube videos, incorporating intra-utterance code-switching and dialect variations. Experiments showed that code-switching significantly reduces the performance of existing deepfake detectors. Why it matters: This work addresses a critical gap in AI's ability to handle linguistic diversity, particularly in regions where code-switching is prevalent, enhancing the reliability of deepfake detection in real-world scenarios.
The article discusses immersive analytics, which uses VR and AR to visualize data in 3D and embed it into the user's environment, and reviews systems and techniques from the Data Visualisation and Immersive Analytics lab at Monash University. It explores the concept of "embodied sensemaking" and its potential to improve how people work with complex data. Professor Tim Dwyer directs the Data Visualisation and Immersive Analytics Lab at Monash University. Why it matters: Immersive analytics could significantly enhance data comprehension and decision-making across various sectors in the Middle East, where large-scale projects and smart city initiatives generate vast datasets.