High-accuracy indoor localization and tracking systems are essential for many modern applications and technologies. However, accurate location estimation of moving targets is challenging. This thesis addresses the challenges in indoor localization and tracking systems and proposes several solutions. A novel signal design, which we named Differential Zadoff-Chu, allows us to develop algorithms that accurately estimate the distances of static and moving targets even under random Doppler shifts. The results show that the proposed algorithms outperform the state-of-the-art in terms of both accuracy and complexity.

Backward error analysis is a tool that allows us to understand the effect of errors without knowing exact solutions.

Hidden confounding is a severe problem when interpreting regression or causal parameters, and it may also lead to poor generalization performance for prediction. Adjusting for unobserved confounding is important but challenging when based on observational data only.

Reliable, robust and interpretable machine learning is a big emerging theme in data science and artificial intelligence, complementing the development of pure black box prediction algorithms. Looking through the lens of statistical causality and exploiting a probabilistic invariance property opens up new paths and opportunities for enhanced interpretation, robustness and external validity, with wide-ranging prospects for various applications.

The talk will discuss the challenging problem of designing Deep Neural Network systems that achieve high performance under low power envelopes, hindering their deployment in the embedded space.

In this talk we will present the design and implementation of monolithic and hybrid sensors using integrated circuits.

Filtering (or data assimilation) is the process of combining noisy observational data with an output of a numerical model to produce an optimal estimate of the evolving state of the system.

The seminar is an overview on my research path and approach. I will start with my initial research on the concept of intrusion tolerance and present some of the results I had at the time. Then, I will show how this initial research had led me to other areas and related approaches: software security, trusted computing, intrusion detection, intrusion recovery, and blockchain interoperability. I will present with some detail very recent research on securely moving data between trusted execution environments (TEEs) in a non-interactive way, a problem that must be solved for digital id crypto wallets starting to appear in Europe and elsewhere.

Epitaxially grown quantum dot (QD) lasers are emerging as an economical approach to obtain on-chip light sources. Thanks to the three-dimensional confinement of carriers, QDs show greatly improved tolerance to defects and promise other advantages such as low transparency current density, high temperature operation, isolator-free operation, and enhanced four-wave-mixing.

Sparse tensors appear frequently in distributed deep learning, either as a direct artifact of the deep neural network's gradients, or as a result of an explicit sparsification process. Most communication primitives are agnostic to the peculiarities of deep learning; consequently, they impose unnecessary communication overhead.

This talk will introduce and motivate a percolation theoretic approach to characterize and thwart malware diffusion in dense wireless networks. First, a brief tutorial on percolation theory will be introduced. Then, the talk will explain the utilization of percolation theory to develop security countermeasures for malware epidemics in 5G and beyond networks. 

The electroencephalogram (EEG) is an important neuroimaging modality to measure neuronal activity of the human brain.

Skyline computation is an increasingly popular query, with broad applicability to many domains. Given the trend to outsource databases, and due to the sensitive nature of the data (e.g., in healthcare), it is essential to evaluate skylines on encrypted datasets.

Breakthroughs such as high-throughput sequencing are generating flourishing high-dimensional data that provoke challenges in both statistical analyses and interpretations.

When constructing high-order schemes for solving hyperbolic conservation laws with multi-dimensional finite volume schemes, the corresponding high-order reconstructions are commonly performed in characteristic spaces to eliminate spurious oscillations as much as possible.

The 2016 Global Burden of Disease Study identifies chronic kidney disease (CKD) as the 11th most common cause of death globally, accounting for 2.17 % of deaths globally, and the 4th most common cause of death in the KSA, accounting for 4.04% of death. The preferred treatment for end-stage renal disease is kidney transplantation. Transplantation is more effective and more cost-effective than dialysis. Unfortunately, living transplant kidney donors are often incompatible with their intended recipients. Kidney Paired Donation programs circumvent these barriers as they enable patients to exchange donors. We aim to build the analytic ground for the Saudi National Paired Donation Program providing a better quality of life for the patients and net savings. This workshop brings together Saudi clinicians and KAUST scientists to discuss modern developments in Kidney Paired Donation along with strategies and research directions for Saudi National Kidney Paired Donation Program.

Many alternatives to the probabilistic modelling of information have been proposed since the birth of modern Statistics; yet, few have been successfully applied to the complex inference problems that modern Statisticians are faced with.

The life sciences have invested significant resources in the development and application of semantic technologies to make research data accessible and interlinked, and to enable the integration and analysis of data. Utilizing the semantics associated with research data in data analysis approaches is often challenging. Now, novel methods are becoming available that combine symbolic methods and statistical methods in Artificial Intelligence. In my talk, I will show how to incorporate biological background knowledge in machine learning models for identification of gene-disease associations, genomic variants that are causative for heritable disorders, and to predict protein functions. The methods I describe are generic and can be applied in other domains in which biomedical ontologies and structured knowledge bases exist.

This talk will give an overview of research activity of engineering different types of complex systems into technological applications in energy harvesting, material science, artificial intelligence and bio-imaging.

Fast and accurate hourly forecasts of wind speed and power are crucial in quantifying and planning the energy budget in the electric grid.

This talk will give an overview of the research of the Secure Next Generation Resilient Systems (SENTRY) lab (sentry.kaust.edu.sa) at KAUST.

The paradigm of Internet of Underwater Things (IoUT) is expected to enable various practical applications such as environmental monitoring, underwater exploration, and disaster prevention. Supporting the concept of IoUT requires robust underwater wireless communication infrastructure. Optical wireless communication has the superiority of wide bandwidth, low latency and high data capacity over its counterparts namely, acoustic and radio frequency.

Available as dedicated hardware components into several mobile and server-grade processors, and recently included in infrastructure-as-a-service commercial offerings by several cloud providers, TEEs allow applications with high privacy and confidentiality demands to be deployed and executed over untrusted environments, shielding data and code from compromised systems or powerful attackers. After an  introduction to basic concepts for TEEs, I will survey some of our most recent contributions exploiting TEEs, including as defensive tools in the context of Federated Learning, as support to build secure cache systems for edge networks, as protection mechanisms in a med-tech/e-health context,  shielding novel environments (ie, WebAssembly), and more. Finally, I will highlight some of the lessons learned and offer open perspectives, hopefully useful and inspirational to future researchers and practitioners entering this exciting area of research.

In the classical theory of the finite element approximation of elliptic partial differential equations, based on standard Galerkin schemes, the energy norm of the error decays with the same rate of convergence as the best finite element approximation, without any additional requirements on the involved spaces.