Hessian-dependent functionals play a pivotal role in a wide latitude of problems in mathematics. Arising in the context of differential geometry and probability theory, this class of problems find applications in the mechanics of deformable media (mostly in elasticity theory) and the modelling of slow viscous fluids. We study such functionals from three distinct perspectives.

Democratizing NLP across numerous languages is a non-trivial task, as it may encounter challenges related to data scarcity, limitations in computational resources, and the intricacies of multilingual and multicultural diversity. The speaker will discuss the efforts and findings in tackling these challenges in this talk. To begin, data scarcity and inconsistency in metadata present common obstacles in low-resource NLP, complicating the understanding of the NLP landscape for low-resource languages.

Remote Direct Memory Access (RDMA) has long been recognized as a powerful technology for high-performance computing and data-intensive applications. In this talk, I will present our experience in deploying intra-region RDMA to support storage workloads in Azure.

The emergence of large language models in text generation has markedly transformed our technological environment, significantly impacting our daily digital interactions.

The electric grid is the backbone of our society and economy. It powers our homes, businesses, and transportation systems. With the advances in technology and the increasing use of renewables, the 3D era (decarbonization, decentralization, digitization) of power systems is facing new challenges. I will discuss how such challenges drive power grid evolution and how the temporal fluctuations of renewable sources impact the grid’s vulnerability. I will also provide methods how we are addressing these threats to ensure that the grid remains secure and resilient. I will conclude my talk with a brief description of my future research plans and a few slides about my research supervision, teaching activities, and visibility of my research group.

The graduate seminar planned for March 24, from 12:00 to 13:00, has been canceled.

In this work, we employ importance sampling (IS) techniques to track a small over-threshold probability of a running maximum associated with the solution of a stochastic differential equation (SDE) within the framework of ensemble Kalman filtering (EnKF).

Artificial materials represent composite media that can be meticulously engineered to exhibit unique wave propagation behaviors. Our research endeavors are driven by the intriguing principles underlying these materials, such as effective models, and their broad applications, including perfect absorption. In this presentation, I will outline our recent advancements in our innovative design strategies for novel artificial materials from both forward first-principle physics-based modeling and data-driven approaches. Specifically, I will highlight our pioneering work in the designs of double-zero-index materials for both electromagnetic and acoustic waves. Additionally, I will discuss our discovery of the acoustic Purcell effect for enhanced emission, as well as our development of analytic and numerical solutions for space-time modulated wave systems. Furthermore, I will delve into our practical solution for achieving broad frequency cloaking of invisibility. These accomplishments hold significant promise for a wide range of applications spanning sound control, communication, sensing, imaging and more.

Datacenters are the cornerstone of our digital lives since they can be viewed as just the other end of our smartphones. From an infrastructure point of view, although they started as a scale-out exercise for commodity off-the-shelf hardware, over the last years we are observing a shift from that paradigm with the emergence of increasingly fast network and storage IO devices, programmable accelerators, and new fast interconnects.

Advances in uncertainty quantification enable more nuanced exploration of decision-making under risk in complex environments.

In this work, we analyze the convergence rate of randomized quasi-Monte Carlo (RQMC) methods under Owen's boundary growth condition [Owen, 2006] via spectral analysis.

The seminars will be delivered in person by Jürgen Schmidhuber, Director of the KAUST AI Initiative, and will run weekly during the spring semester. The program will start with aspects of the theory of computation and delve into many topics not typically covered in a deep learning course. This is a truly unique opportunity to learn from one of the founders in artificial intelligence.

In 2019, diabetes caused 1.5 million global deaths, with 48% occurring before age 70. While Type 1 diabetes strongly depends on genetic components and is usually diagnosed in childhood, Type 2 diabetes is primarily caused by long term consumption of high calories foods. Lifestyle choices significantly influence the risk of Type 2 diabetes and obesity, including energy intake, diet composition, physical activity, and smoking.

To protect privacy of training data for deep learning models, one line of work proposes to use Differential Privacy (DP). Over recent years, a substantial body of research has emerged, proposing a diverse array of differentially private training algorithms tailored to various deep learning models.

Traditional guiding structures and microwave packaging have limitations regarding losses or physical realization. Therefore, there is a need for efficient millimeter-wave guiding structures that overcome such limitations. Gap waveguide technology is found to overcome such limitations at millimeter-wave bands. Interest in such technology is increasing.

 

Abstract

The talk will give an overview of research at the Department of Computer

When pressure acoustic waves interact with rotating scatterers, they undergo peculiar and intriguing characteristics. In this talk, I will discuss our recent findings on the physics of acoustic scattering and propagation in spinning fluids.

We present a theoretical analysis of the Weak Adversarial Networks (WAN) method, recently proposed in [1, 2], as a method for approximating the solution of partial differential equations in high dimensions and tested in the framework of inverse problems. In a very general abstract framework.

Most existing AI learning methods can be categorized into supervised, semi-supervised, and unsupervised methods. These approaches rely on defining empirical risks or losses on the provided labeled and/or unlabeled data.

Hajj is a very complex event that requires huge logistical organization. Mobility is one of the most important logistical services needed since Hajj rituals are based on movement from one location to another.

Quadratic Forms in Gaussian Random Variables (QFGRV) appear in a wide variety of applications in signal processing, communications, performance and reliability engineering, genetics, etc.

Analogies between financial markets and critical phenomena have long been observed empirically. So far, no convincing theory has emerged that can explain these empirical observations. Here, we take a step towards such a theory by modeling financial markets as a lattice gas.

Living in a computer-reliant era, we’re balancing the power of computer systems with the challenges of ensuring their functional correctness and security. Program analysis has proven successful in addressing these issues by predicting the behavior of a system when executed.

The research focuses on improving metal-GaN and dielectric-GaN interfaces for high-performance GaN-based electronics. For metal-GaN, the damage caused by e-beam evaporation was mitigated using Ti3C2Tx MXene films, achieving a record ION/IOFF current of 1013 and low subthreshold swing.

Abstract