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 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.
Prof. Daniel Panario gave a seminar on irreducible polynomials over finite fields and their applications in cryptography. The seminar covered how finite fields are used as basic components in many cryptographic applications. It surveyed families of irreducible polynomials and commented on their properties. Why it matters: The talk highlights the mathematical foundations and ongoing research relevant to cryptographic implementations in the region.
Researchers at ETH Zurich have formalized models of the EMV payment protocol using the Tamarin model checker. They discovered flaws allowing attackers to bypass PIN requirements for high-value purchases on EMV cards like Mastercard and Visa. The team also collaborated with an EMV consortium member to verify the improved EMV Kernel C-8 protocol. Why it matters: This research highlights the importance of formal methods in identifying critical vulnerabilities in widely used payment systems, potentially impacting financial security for consumers in the GCC region and worldwide.
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.
KAUST Ph.D. students David Evangelista and Xianjin Yang won best paper awards at international conferences this summer for their work in mean-field game theory. Evangelista's paper focused on solutions for stationary mean-field games with congestion, while Yang's paper developed numerical methods for homogenization problems. The awards were presented at the 18th International Symposium on Dynamic Games and Applications in France and the 12th American Institute of Mathematical Sciences (AIMS) Conference in Taiwan. Why it matters: The recognition highlights KAUST's strength in applied mathematics and computational science, specifically in the emerging field of mean-field games with applications across various domains.
This paper introduces DaringFed, a novel dynamic Bayesian persuasion pricing mechanism for online federated learning (OFL) that addresses the challenge of two-sided incomplete information (TII) regarding resources. It formulates the interaction between the server and clients as a dynamic signaling and pricing allocation problem within a Bayesian persuasion game, demonstrating the existence of a unique Bayesian persuasion Nash equilibrium. Evaluations on real and synthetic datasets demonstrate that DaringFed optimizes accuracy and convergence speed and improves the server's utility.
Alexander Gasnikov from the Moscow Institute of Physics and Technology presented a talk on open problems in convex optimization. The talk covered stochastic averaging vs stochastic average approximation, saddle-point problems and accelerated methods, homogeneous federated learning, and decentralized optimization. Gasnikov's research focuses on optimization algorithms and he has published in NeurIPS, ICML, EJOR, OMS, and JOTA. Why it matters: While the talk itself isn't directly related to GCC AI, understanding convex optimization is crucial for advancing machine learning algorithms used in the region.