KAUST's Stochastic Numerics Research Group is developing methods for pricing European options. Their approach, detailed in an upcoming Journal of Computational Finance article, focuses on systematically tuning parameters to achieve accuracy while minimizing computational effort. The goal is to enable automated computation of fair prices for options contracts, similar to how insurance companies determine premiums. Why it matters: This research advances computational finance in the region, potentially improving risk management and investment strategies.
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 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 Ph.D. student Chiheb Ben Hammouda won the best poster award at the Society for Industrial and Applied Mathematics Conference on Financial Mathematics & Engineering (FM19) for his work on option pricing under the rough Bergomi model. The winning poster, titled "Hierarchical adaptive sparse grids and quasi-Monte Carlo for option pricing under the rough Bergomi model," details research carried out under the supervision of KAUST Professor Raul Tempone. The research group designed new efficient numerical methods for pricing derivatives under the rough Bergomi model by combining smoothing techniques. Why it matters: This award highlights KAUST's growing expertise in financial mathematics and its contribution to solving complex problems in the field using advanced numerical methods.
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.
KAUST Professor of Applied Mathematics and Computational Science, Dr. Peter Markowich, has been named a 2020 Fellow to the European Academy of Sciences. This recognizes his work in the mathematical and numerical analysis of partial differential equations. Markowich joined KAUST in 2011 and has contributed to over 270 projects worldwide. Why it matters: This honor brings recognition to KAUST's faculty and highlights the university's contribution to advanced mathematical research with applications across science and engineering.
KAUST Professor Peter Markowich has been named a 2022 Fellow of the American Mathematical Society (AMS). He is recognized for contributions to partial differential equations, particularly the mathematical and numerical analysis of dispersive equations. Markowich applies differential mathematics to disciplines such as physics, AI, biology and engineering, including research on leaf venation patterns. Why it matters: This recognition highlights KAUST's strength in applied mathematics and its faculty's contributions to both theoretical and interdisciplinary research.
MBZUAI researchers presented a study at NAACL 2024 analyzing errors made by open-source LLMs when solving math word problems. The study, led by Ekaterina Kochmar and KV Aditya Srivatsa, investigates characteristics that make math word problems difficult for machines. Llama2-70B was used to test the ability of LLMs to solve these problems, revealing that LLMs can perform math operations correctly but still give the wrong answer. Why it matters: The research aims to improve AI's ability to understand and solve math word problems, potentially leading to better educational applications and teaching methods.