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.
The article discusses the importance of sample correlations in computer graphics, vision, and machine learning, highlighting how tailored randomness can improve the efficiency of existing models. It covers various correlations studied in computer graphics and tools to characterize them, including the use of neural networks for developing different correlations. Gurprit Singh from the Max Planck Institute for Informatics will be presenting on the topic. Why it matters: Optimizing sampling techniques via understanding and applying correlations can lead to significant advancements and efficiency gains across multiple AI fields.
Amir Goharshady from Hong Kong University of Science and Technology presented a talk at MBZUAI on martingale-based verification of probabilistic programs. The talk covered using martingale-based approaches for proving termination and synthesizing cost bounds for probabilistic programs, automating program analysis with template-based methods. He also discussed remaining challenges and open problems in the area. Why it matters: Advances in formal verification and analysis of probabilistic programs are crucial for ensuring the reliability and safety of AI systems that rely on randomization.
MBZUAI is hosting an "AI Quorum on Statistics for the Future of AI" in Abu Dhabi, focusing on the intersection of statistics and AI in healthcare. Organized by Professors Tian Zheng (Columbia University) and Hongtu Zhu (UNC), the event gathers experts from top global universities and organizations like Eli Lilly and MD Anderson Cancer Center. The workshop aims to integrate statistical insights into AI research, fostering innovations in the field. Why it matters: By convening international experts, MBZUAI is positioning itself as a hub for interdisciplinary AI research with a focus on healthcare applications.
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'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 Ph.D. student Jian Cao received a best paper award from the American Statistical Association (ASA) for his paper on computing high-dimensional normal and Student-t probabilities. The paper uses Tile-Low-Rank Quasi-Monte Carlo and Block Reordering. Cao, a member of Professor Marc Genton's group, will be recognized at the ASA's Joint Statistical Meetings. Why it matters: This award highlights KAUST's strength in high-performance computing and statistical research, contributing to advancements in handling complex, high-dimensional datasets.
KAUST Professor Peter Markowich discusses the role of mathematics in football, describing a match as a random process with a drift. The randomness stems from player conditions, referee decisions, weather, and more, while the drift represents the higher probability of the better team winning. He notes that the complexity arising from 11 players on each side increases the randomness compared to sports like tennis. Why it matters: This perspective highlights the interplay of chance and skill in sports, offering a mathematical lens for understanding game dynamics.