Professor Omar Knio, Dean of the Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division at KAUST, has been named a 2026 SIAM Fellow. This prestigious recognition from the Society for Industrial and Applied Mathematics honors his leadership in uncertainty quantification and multiscale mathematics. His research areas include applications in combustion, energetic materials, geophysical fluid dynamics, high-performance computing, and data-enabled predictive science. Why it matters: This recognition highlights KAUST's global standing in applied mathematics and computational science, reinforcing its role as a hub for scientific talent and interdisciplinary research crucial for advanced technological development in Saudi Arabia.
Researchers from MBZUAI developed "uncertainty quantification heads" (UQ heads) to detect hallucinations in language models by probing internal states and estimating the credibility of generated text. UQ heads leverage attention maps and logits to identify potential hallucinations without altering the model's generation process or relying on external knowledge. The team found that UQ heads achieved state-of-the-art performance in claim-level hallucination detection across different domains and languages. Why it matters: This approach offers a more efficient and accurate method for identifying hallucinations, improving the reliability and trustworthiness of language models in various applications.
Omar Knio, Dean of the Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division at KAUST, has been named a 2026 SIAM Fellow. He received this honor from the Society for Industrial and Applied Mathematics (SIAM) for his leadership in uncertainty quantification and multiscale mathematics. His research applies to fields such as combustion, energetic materials, and geophysical fluid dynamics. Why it matters: This recognition highlights KAUST's and Saudi Arabia's growing contributions to global scientific talent and interdisciplinary research in applied mathematics and computational science.
A research paper co-authored by Dr. Maxim Panov and Kirill Fedyanin from the AI and Digital Science Research Center (AIDRC) has been accepted for publication at NeurIPS 2022. The paper, titled “Nonparametric Uncertainty Quantification for Single Deterministic Neural Network”, proposes a fast and scalable method for uncertainty quantification in ML models. The method disentangles aleatoric and epistemic uncertainties and was validated on text classification and image datasets including MNIST and ImageNet. Why it matters: This demonstrates the growing AI research capabilities and contributions from the UAE to the global AI community, particularly in fundamental machine learning research.
KAUST Professor Raul Tempone, an expert in Uncertainty Quantification (UQ), has been appointed as an Alexander von Humboldt Professor at RWTH Aachen University in Germany. This professorship will enable him to further his research on mathematics for uncertainty quantification with new collaborators. Tempone believes the KAUST Strategic Initiative for Uncertainty Quantification (SRI-UQ) contributed to this award. Why it matters: This appointment enhances KAUST's visibility and facilitates cross-fertilization between European and KAUST research groups, benefiting both institutions and attracting talent.
KAUST Professor Raul Tempone received the Alexander von Humboldt Professorship at RWTH Aachen in Germany. The award recognizes Tempone's international leadership in mathematics for uncertainty quantification. The professorship includes up to 5 million euros in funding from the German Federal Ministry of Education and Research. Why it matters: This award highlights KAUST's strength in applied mathematics and its relevance to simulation-based decision-making with potential benefits for Saudi Arabia and the world.
KAUST recently hosted the Research Conference: Predictive Complex Computational Fluid Dynamics (PCCFD) from May 22 to 24. The conference brought together local and international CFD scientists from academia and industry to discuss the latest work and findings in CFD. Topics included variable-order algorithms, adaptive mesh refinement, fluid-structure interaction, and uncertainty quantification. Why it matters: The conference highlights KAUST's commitment to advancing CFD research and its applications in various fields, including aerospace, oil industry, and environmental science.
KAUST hosted the Advances in Uncertainty Quantification Methods, Algorithms and Applications conference (UQAW2016) in January 2016. The event featured 75 presentations and 20 invited speakers from various countries. Professor Raul Tempone presented research on computational approaches to fouling accumulation and wear degradation using stochastic differential equations. Why it matters: This work provides a new computational approach based on stochastic differential equations to predict fouling patterns of heat exchangers which can optimize maintenance operations and reduce engine shut-down periods.