The talk will present our efforts to develop the next generation operational systems for the Red Sea and the Arabian Gulf, as part of Aramco’s resolution toward the Fourth Industrial Revolution. These integrated systems, we refer to as iReds and iGulf, have been built around state-of-the-art ocean-atmosphere-wave general circulation models that have been specifically developed for the region and nested within the global weather systems.

Imaging systems have long been designed in separated steps: the experience-driven optical design followed by sophisticated image processing. Such a general-propose approach achieves success in the past but left the question open for specific tasks and the best compromise between optics and post-processing, as well as minimizing costs. Driven from this, a series of works are proposed to bring the imaging system design into end-to-end fashion step by step, from joint optics design, PSF optimization, phase map optimization to a general end-to-end complex lens camera.

In this seminar, I will go over our journey in the underwater networks research work. Basically, I will highlight our recent work on bringing the Internet to underwater environments by deploying a low power and compact underwater optical wireless system, called Aqua-Fi, that support today’s Internet applications.

In this talk, I will introduce a simulator specifically formulated and implemented for photoconductive terahertz devices enhanced with nanostructures. The fundamental challenge in formulating this simulator is the rigorous modeling of the coupling mentioned above. My talk will focus on this aspect as well as the general concerns in building mathematical models for various physical processes and their numerical discretization. To this end, I will introduce a discontinuous Galerkin framework that can efficiently discretize the multiscale mathematical models formulated for coupled semiconductor physics and electromagnetics. Furthermore, I will present numerical results which demonstrate the applicability of this framework in characterizations of photoconductive terahertz antennas and photomixers.

As a fundamental problem in both machine learning and privacy, Empirical Risk Minimization in the Differential Privacy Model (DP-ERM) received much attentions. However, most of the previous studies are either in the central DP model or interactive LDP model. In this talk, I will discuss some recent developments of DP-ERM in the non-interactive LDP model.

In this talk I report some results on the approximation of fluid-structure interaction problems using non matching grids. Our formulation originates from the Immersed Boundary Method and then moved toward the Fictitious Domain approach. The advantages of this formulation is that it avoids the difficulties related with mesh generation and it allows the treatment of fluid and solid in their natural Eulerian and Lagrangian framework. I present the well-posedness of our formulation at the continuous level in a simplified setting. Moreover, I shall discuss various time discretizations that provide unconditionally stable schemes and some computational details.

Computational imaging differs from traditional imaging systems by integrating an encoded measurement system and a tailored computational algorithm to extract interesting scene features. This dissertation demonstrates two approaches which apply computational imaging methods to the fluid domain. In the first approach, we study the problem of reconstructing time-varying 3D-3C fluid velocity vector fields. We extend 2D Particle Imaging Velocimetry to three dimensions by encoding depth into color.

The mobile revolution of the past decade led to the ubiquitous presence of location data in all application domains, ranging from public safety and healthcare to urban planning, transportation and commercial applications. Numerous services rely on location data to provide customized service to their users. At the same time, there are serious concerns with respect to protecting individual privacy, as location traces can disclose sensitive details to an untrusted service.

This talk will be focused on developing low-cost sensors, which can increase oil production efficiency through real-time monitoring of oil wells. The mechanism behind the development of low-cost & printed microwave sensors will be introduced followed by a number of applications where these sensors can be utilized. A specific application to measure the composition of production fluid will be described in detail. Unique benefits of Microwave DMOR technology, utilized in the sensors, will be explained in comparison with existing technologies.

Modeling, estimation and prediction of spatial extremes is key for risk assessment in a wide range of geo-environmental, geo-physical, and climate science applications. In this work, we propose a flexible approach for modeling and estimating extreme sea surface temperature (SST) hotspots, i.e., high threshold exceedance regions, for the whole Red Sea, a vital region of high biodiversity.

We consider statistical inference for a class of agent-based SIS and SIR models. In these models, agents infect one another according to random contacts made over a social network, with an infection rate that depends on individual attributes. Infected agents might recover according to another random mechanism that also depends on individual attributes, and observations might involve occasional noisy measurements of the number of infected agents. Likelihood-based inference for such models presents various computational challenges. In this talk, I will present various sequential Monte Carlo algorithms to address these challenges.

This talk will give an overview of the research of the High-Performance Visualization research group (vccvisualization.org) at the KAUST Visual Computing Center (VCC). Interactive visualization is crucial to exploring, analyzing, and understanding large-scale scientific data, such as the data acquired in medicine or neurobiology using computed tomography or electron microscopy, and data resulting from large-scale simulations such as fluid flow in the Earth’s atmosphere and oceans. The amount of data in data-driven science is increasing rapidly toward the petascale and further.

After a quick overview of the ECE Graduate Seminar logistics, I will share a quick introduction to the wellbore construction process. This will help build the case for maintaining wellbore integrity in order to protect assets, people, the environment and production. The synergistic integration of electromagnetics, electronics and machine learning to create a novel mechatronic solution to address wellbore integrity needs is then discussed. The solution utilizes a full maxwell equations solver deployed on KAUST’s super computing platforms to enable next generation physics informed wellbore integrity solutions based on non-contact EM field propagation circuits. While downhole camera technologies are used today, they require illumination and an optically clear environment. Our electromagnetic ‘vision’ system overcomes these limitations and provides additional capability to ‘see through’ nested wellbore tubulars.

In this talk, I will describe three use cases that highlight present challenges and opportunities for the development of machine learning methodology for applications in healthcare. First, I will describe the development of simple word embedding approaches for bag of-documents classification and its applications to diagnosis of peripheral artery disease from clinical narratives. Second, I will present an approach for volumetric image classification that leverages attention mechanisms, contrastive learning and feature-encoding sharing for geographic atrophy prognosis from optical coherence tomography images. Third, I will discuss machine learning approaches for multi-modal and multi-dataset integration for biomarker discovery from molecular (omics) data. To conclude, I will summarize the contributions and insights in each of these different directions in which relatively low sample sizes are the common denominator.

Magnetic random access memory (MRAM) devices have been widely studied since the 1960s. During this time, the size of spintronic devices has continued to decrease. Consequently, there is now an urgent need for new low-dimensional magnetic materials to mimic the traditional structures of spintronics at the nanoscale. We also require new effective mechanisms to conduct the main functions of memory devices, which are: reading, writing, and storing data.

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This dissertation tackles the problem of entanglement in Generative Adversarial Networks (GANs). The key insight is that disentanglement in GANs can be improved by differentiating between the content, and the operations performed on that content. For example, the identity of a generated face can be thought of as the content, while the lighting conditions can be thought of as the operations.

This event has been postponed from 20th July to 27th July. Stochastic optimization refers to the minimization/maximization of an objective function in the presence of randomness. The randomness may appear in objective functions, constraints, or optimization methods. It has the advantage of dealing with uncertainties that deterministic optimizers cannot solve or cannot solve efficiently. In this work, we discuss the implementation of stochastic optimization methods in solving target positioning problems and tackling key issues in location-based applications.

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High Dynamic Range (HDR) image acquisition from a single image capture, also known as snapshot HDR imaging, is challenging because the bit depths of camera sensors are far from sufficient to cover the full dynamic range of the scene. Existing HDR techniques focus either on algorithmic reconstruction or hardware modification to extend the dynamic range. In this thesis, we propose a joint design for snapshot HDR imaging by devising a spatially varying modulation mask in the hardware combined with a deep learning algorithm to reconstruct the HDR image. In this approach, we achieve a reconfigurable HDR camera design that does not require custom sensors, and instead can be reconfigured between HDR and conventional mode with very simple calibration steps. We demonstrate that the proposed hardware-software solution offers a flexible, yet robust, way to modulate per-pixel exposures, and the network requires little knowledge of the hardware to faithfully reconstruct the HDR image. Comparative analysis demonstrated that our method outperforms the state-of-the-art in terms of visual perception quality.