In this thesis, we develop new flexible sub-asymptotic extreme value models for modeling spatial and spatio-temporal extremes that are combined with carefully designed gradient-based Markov chain Monte Carlo (MCMC) sampling schemes and that can be exploited to address important scientific questions related to risk assessment in a wide range of environmental applications. The methodological developments are centered around two distinct themes, namely (i) sub-asymptotic Bayesian models for extremes; and (ii) flexible marked point process models with sub-asymptotic marks. In the first part, we develop several types of new flexible models for light-tailed and heavy-tailed data, which extend a hierarchical representation of the classical generalized Pareto (GP) limit for threshold exceedances. Spatial dependence is modeled through latent processes. We study the theoretical properties of our new methodology and demonstrate it by simulation and applications to precipitation extremes in both Germany and Spain.

In this thesis, we investigate how learning-based approaches are implemented to solve the communication network problems and how communication network dependencies impact the training of learning-based approaches.

The statistical modeling of extreme natural hazards is becoming increasingly important due to climate change, whose effects have been increasingly visible throughout the last decades. It is thus crucial to understand the dependence structure of rare, high-impact events over space and time for realistic risk assessment. For spatial extremes, max-stable processes have played a central role in modeling block maxima. However, the spatial tail dependence strength is persistent across quantile levels in those models, which is often not realistic in practice. This lack of flexibility implies that max-stable processes cannot capture weakening dependence at increasingly extreme levels, resulting in a drastic overestimation of joint tail risk. 

DNA Nanotechnology is a fascinating field that studies how to construct small biological structures entirely from DNA as a building material. The key insight is that DNA, if designed in a particular way, can construct complex 3D nanoscale structures entirely by means of self-assembly, governed by the base-pairing principle.

With the advent of wearable sensors and internet of things (IoT) applications, there is a new focus on electronics which can be compact, light-weight and flexible so that these can be worn or mounted on non-planar objects. Due to large volume (billions of devices), it is required that the cost is extremely low, to the extent that they become disposable. In the context of miniaturization and lower cost, concepts such as System-on-chip (SoC) where a complete system is realized on a single chip (integrated circuit (IC)) or System-on-package (SoP) where the package of the chip is made functional are beneficial. The flexible and low-cost aspects can be addressed through additive manufacturing technologies such as inkjet and screen printing. Two important aspects of any IoT system, “Sensing” and “Wireless Communication”, will be the focus of this talk. The SoC part of the talk will focus on integration of the antenna on the chip and ways of enhancing its efficiency despite the lossy Silicon substrate in conventional semiconductor manufacturing processes. Through a SoP design example, it will be shown how smart packaging of a chip can boost the performance without adding any additional components or cost. In the later part of the talk, additive manufacturing will be introduced as an emerging technique to realize low cost and flexible wireless communication and sensing systems. Various novel functional inks, such as conductive, dielectric, phase change and sensing materials will be shown. A multilayer process will be presented where dielectrics are also printed in addition to the metallic parts, thus demonstrating fully printed components. Finally, some printed sensor examples will be shown for remote health and environmental monitoring. The promising results of these designs indicate that the day when electronics can be printed like newspapers and magazines through roll-to-roll printing is not far away.

Organic mixed (ionic and electronic) charge conductors and devices offer a new toolbox for interfacing with biological systems.

We introduce ProxSkip a surprisingly simple and provably efficient method for minimizing the sum of a smooth (ƒ) and an expensive nonsmooth proximable (ψ) function.

Multi-label learning addresses the problem that one instance can be associated with multiple labels simultaneously. More or less, these labels are usually dependent on each other in different ways. Understanding and exploiting the Label Dependency (LD) is well-accepted as the key to build high-performance multi-label classifiers, i.e., classifiers having abilities including but not limited to generalizing well on clean data and being robust under evasion attack.

Topological data analysis (TDA) studies the shape patterns of data. Persistent homology (PH) is a widely used method in TDA that summarizes homological features of data at multiple scales and stores them in persistence diagrams (PDs). In this talk we will discuss a random persistence diagram generation (RPDG) method that generates a sequence of random PDs from the ones produced by the data. RPDG is underpinned by (i) a model based on pairwise interacting point processes for inference of persistence diagrams, and (ii) by a reversible jump Markov chain Monte Carlo (RJ-MCMC) algorithm for generating samples of PDs. An example on a materials science problem will demonstrate the applicability of the RPDG method.

Data in many modern signal processing, machine learning, and statistics problems tend to have tensor (aka, multidimensional / multiway array) structure. While traditional approaches to processing of such data involve 'flattening' of data samples into vectors, it has long been realized that explicit exploitation of tensor structure of data can lead to improved performance. Recent years, in particular, have witnessed a flurry of research activity centered around development of computational algorithms for improved processing of tensor data.

Knowing metastasis is the primary cause of cancer-related deaths incentivized research to unravel the complex cellular processes that drive the metastasis. Advancement in technology and specifically the advent of high-throughput sequencing provides knowledge of such processes. This knowledge led to the development of therapeutic and clinical applications. In this regard, predicting metastasis onset has also been explored using artificial intelligence (AI) approaches that are machine learning (ML), and more recently, deep learning (DL).

Visual Computing has become a key enabling technology for a diverse set of applications spanning scientific discovery, digital services, medicine, robotics, consumer electronics, and entertainment, to name just a few. The research community tackles problems in this vast space by drawing from expertise in multiple disciplines, including Computer Science, Electrical Engineering, and Mathematics. The KAUST Masterclass on Visual Computing highlights a selection of cutting-edge academic research within this field by comprising a series of talks focusing on different topics ranging from Computational Architecture and Fabrication, Deep Optics, and Generative Modeling, to Nanovisualization, Physics-based simulation, and Representation Learning.

Monolithic matrix-free solver for fluid-structure interaction problems.

This dissertation introduces flexible, lightweight, and robust Laser-Scribed Graphene (LSG) sensor solutions for detecting various physical parameters, such as strain, flow, deflection, force, pressure, temperature, conductivity, and magnetic field. Multifunctionality was obtained by exploiting the direct laser scribing process combined with the flexible nature of polyimide and the piezoresistivity of porous graphene. The outstanding properties of LSG, such as low cytotoxicity, biocompatibility, corrosion resistance, and ability to function under extreme pressure and temperature conditions, allowed targeting diverse emerging applications.

Traditional computing systems separate processors from memory, performing computation by shuttling data back and forth between these two units all the time. This bottleneck incurs limited processing speed and high power consumption in computing systems for deep learning models of ever-increasing complexity. Novel approaches and new principles are needed to revolutionize computing systems. Neuromorphic systems are proposed as a new computing architecture based on spiking neural networks analogous to the existing nervous systems.

We are excited to announce the KAUST Research Conference on Flow simulation at the exascale: Opportunities, challenges, and its application in the industry, which will be held on March 28 – March 30, 2022 (#ExaCFDKAUST). The conference aims to bring together experts in flow simulation, computational mathematics, and high-performance computing. The goal is to define a research agenda and path forward that will enable scientists and engineers to continually leverage, engage, and direct advances in computational systems on the path to exascale computing and beyond. The conference will give space for presentations and discussions of computational fluid dynamics. As part of this event, we are accepting poster abstract submissions. The poster should present high-quality research contributions describing original and unpublished results of conceptual, constructive, empirical, experimental, or theoretical work in all areas of Computational Fluid Dynamics. For more information please visit the conference website.

Principal Component Analysis (PCA) is a fundamental data preprocessing tool in the world of machine learning.

Abstract

In this talk we will present two classes of regression models.

A standard way to move particles in a SMC sampler is to apply several steps of a MCMC (Markov chain Monte Carlo) kernel. Unfortunately, it is not clear how many steps need to be performed for optimal performance. In addition, the output of the intermediate steps are discarded and thus wasted somehow. We propose a new, waste-free SMC algorithm which uses the outputs of all these intermediate MCMC steps as particles. We establish that its output is consistent and asymptotically normal. We use the expression of the asymptotic variance to develop various insights on how to implement the algorithm in practice. We develop in particular a method to estimate, from a single run of the algorithm, the asymptotic variance of any particle estimate. We show empirically, through a range of numerical examples, that waste-free SMC tends to outperform standard SMC samplers, and especially so in situations where the mixing of the considered MCMC kernels decrease across iterations (as in tempering or rare event problems).

With the algorithm's suitability for exploiting current petascale and next-generation exascale supercomputers, stable and structure-preserving properties are necessary to develop predictive computational tools. This dissertation uses the mimetic properties of SBP-SAT operators and the structure-preserving property of a new relaxation procedure for Runge--Kutta schemes to construct nonlinearly stable full discretizations for non-reactive compressible computational fluid dynamics (CFD) and reaction-diffusion models.

We study the MARINA method of Gorbunov et al (ICML 2021) - the current state-of-the-art distributed non-convex optimization method in terms of theoretical communication complexity. Theoretical superiority of this method can be largely attributed to two sources: the use of a carefully engineered biased stochastic gradient estimator, which leads to a reduction in the number of communication rounds, and the reliance on {\em independent} stochastic communication compression operators, which leads to a reduction in the number of transmitted bits within each communication round.

The technological evolution has led to the current high-performing wireless communication systems that we use on a daily basis. However, coping with the increasing demand is becoming more and more challenging, especially since we are approaching the limits of what can be done with the available resources. One of these resources is bandwidth. This spectrum scarcity problem has motivated researchers to explore new frequencies for wireless communications. Due to this reason, the upper radio-frequency (RF) spectrum, from mmWave and THz to optical bands, is being pursued, which is termed as “Extreme Bandwidth Communication.” This conference brings world experts and the brightest minds from academia and industry to present the latest trends, challenges, results, and opportunities in the field of extreme bandwidth communication.

This dissertation aims to investigate and address noisy ocean issues and build large-scale underwater sensor networks by optical communication and sensing technology. The dissertation proposes using UWOC and FC&S technology to replace the conventional acoustic communication technology and reduce the noise in the ocean. UWOC helps achieve high-speed wireless communications between sensors, vehicles, and even humans for UIoT. The significant challenges of developing UWOC systems are the complex underwater environment's attenuation, scattering, and turbulence effects. This dissertation studied the turbulence effects on the UWOC system’s performance and addressed the pointing-acquisition-and-tracking issues.

As we grow, expand and experience digital world, we feel the need to interface better with the natural world which is analog.