In this talk, we will illustrate the application of SIM in multiple-input multiple-output (MIMO) and multi-user MIMO wireless communications.

In this talk, I will present a vision of future research topics of long-term importance in the area of Satellite Communications and Non-Terrestrial Networks.

Dear Kaustians, We are excited to announce the upcoming Stochastic Numerics and Statistical Learning: Theory and Applications Workshop 2024, taking place at KAUST, Building 9, from May 19-30, 2024. Following the highly successful 2022 and 2023 editions, this year's workshop promises to be another engaging and insightful event for researchers, faculty members, and students interested in stochastic algorithms, statistical learning, optimization, and approximation. The 2024 workshop aims to build on the achievements of last year's event, which featured 28 talks, two mini-courses, and two poster sessions, attracting over 150 participants from various universities and research institutes. In previous two years attendees had the opportunity to learn from through insightful talks, interactive mini-courses, and vibrant poster sessions. This year, the workshop will once again showcase contributions that offer mathematical foundations for algorithmic analysis or highlight relevant applications. Confirmed speakers include renowned experts from institutions such as Ecole Polytechnique, EPFL, Université Pierre et Marie Curie - Paris VI, and Imperial College London, among others.

Abstract

Despite being small and simple structured in comparison to their victims, virus particles

Organic mixed ion-electron conductors (OMIECs) have found themselves in the spotlight of the bioelectronics field because of their potential to bridge the gap between the worlds of biology and electronics. From the initial discovery of conductive polymers just a few decades prior, the evolution of OMIECs has been growing exponentially.

The Internet of Things (IoT) will require billions of interconnected devices that can communicate with each other and act based on their environment without human supervision.

Nowadays, videos are omnipresent in our daily lives. From TikTok clips to Bilibili videos, from surveillance footage to vlogs recordings, the sheer volume of video content is staggering. Processing and analyzing the substantial volume of video data demands immense human effort. While computer vision techniques have made remarkable progress in automating video understanding in short clips, their effectiveness and efficiency when applied to long-form videos still fall short of the mark.

Rare, low-probability events often lead to the biggest impacts. Therefore, the development of statistical approaches for modeling, predicting and quantifying environmental risks associated with natural hazards is of utmost importance. In this seminar, I will show how statistical deep-learning methods can help solve challenges that arise when modeling complex and massive spatiotemporal extremes data.

Given the complex nature of brain signals and the challenges involved in estimating its dependence and analyzing the emerging topological patterns, this dissertation introduces innovative statistical tools designed to explore both the functional and effective connectivity within brain networks. It sheds light on frequency-specific patterns in ADHD subjects and introduces a novel approach for examining the hierarchical structure of brain regions during seizures. Our work provides a novel perspective on the organization of brain networks and presents insight into how various conditions influence their complex structure.

The structural integrity of wellbore casings and transportation pipelines is a critical aspect in the oil and gas industry for operational efficiency and environmental safety. Traditional non-destructive testing methods, while effective, face significant challenges in accurately assessing and monitoring these crucial infrastructural components, especially under harsh operating environments. Furthermore, the inner volume of these tubular structures and the flow speed of transported liquids pose additional difficulties upon the performance of devices designed to inspect their structures for defects and deformations.

We will discuss the solution of eigenvalue problems associated with partial differential equations (PDE)s that can be written in the generalised form Ax = λMx, where the matrices A and/or M may depend on a scalar parameter. Parameter dependent matrices occur frequently when stabilised formulations are used for the numerical approximation of PDEs. With the help of classical numerical examples we will show that the presence of one (or both) parameters can produce unexpected results.

In this talk, I will discuss our progress in advancing the discovery of catalysts for green hydrogen production and carbon dioxide conversion, as well as designing novel metalorganic frameworks for direct air capture.

With the rapid development of wireless mobile communication technologies, there has been a growing demand for high data-rate communication in the mmWave range of 5G bands and future 6G bands due to their much larger available bandwidths. Despite their potential, these frequency ranges suffer from significant atmospheric attenuation, necessitating antennas with high gain and wide beam-scanning capabilities to ensure robust coverage. Thus, there is a need to develop compact, high gain, wideband, and wide beam-scanning mmWave antenna/array for 5G/6G applications.

To improve the data transmission speed of HTTP, HTTP/2 has extended  features based on HTTP/1.1 such as stream multiplexing. Along with its  wide deployment in popular web servers, numerous vulnerabilities are exposed. Denial of service, one of the most popular HTTP/2 vulnerabilities is attributed to the inappropriate implementations of flow control for stream multiplexing.

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. Beyond extracting learning signals from labeled/unlabeled training data, in this talk, I will cover a class of methods that I have been developing for over a decade, which can learn beyond the vocabulary that was trained on and can compose or create novel concepts.

Malicious software or malware is a serious cybersecurity threat and the research community has explored it extensively for almost three decades. Since it is believed that people are often the weak link in cybersecurity, exploring malware attacks and defenses in the human context can provide new insights into how the threat posed by malware can be addressed.

Growth is a fundamental process in biological systems and various technological applications, including epitaxial deposition and additive manufacturing. The interaction between growth and mechanics in deformable bodies leads to a wealth of very challenging mathematical questions. I will give a short overview of the key concepts of morphoelasticity, namely, the theory of elastic deformations in growing bodies.

Our daily lives are becoming increasingly dependent on smart cyber-physical infrastructures, such as smart homes and cities, smart grid, smart transportation, smart healthcare, smart agriculture, and so on.

We introduce a framework for calibrating machine learning models so that their predictions satisfy explicit, finite-sample statistical guarantees. Our calibration algorithms work with any underlying model and (unknown) data-generating distribution and do not require model refitting.

Artificial intelligence (AI) has focused on a paradigm in which intelligence inheres in a single, autonomous agent. Social issues are entirely secondary in this paradigm. When AI systems are deployed in social contexts, however, the overall design of such systems is often naive --- a centralized entity provides services to passive agents and reaps the rewards. Such a paradigm need not be the dominant paradigm for information technology. In a broader framing, agents are active, they are cooperative, and they wish to obtain value from their participation in learning-based systems. Agents may supply data and other resources to the system, only if it is in their interest to do so. Critically, intelligence inheres as much in the overall system as it does in individual agents, be they humans or computers. This is a perspective that is familiar in the social sciences, and a key theme in my work is that of bringing economics into contact with foundational issues in computing and data sciences. I'll emphasize some of the mathematical challenges that arise at this tripartite interface.

Ontologies and Knowledge Graphs are becoming increasingly popular for knowledge representation and reasoning, with a fundamental role in AI and Information Systems.

A 100% renewable-based power system requires higher energy flexibility than conventional grids. ENOWA is developing an Energy Flexible Manufacturing Design Service in collaboration with OXAGON’s Advanced and Clean Manufacturing.

Networks are nowadays pervasive in Big Data. It is often useful to regroup such data in clusters according to distinctive node features and use a representative element for each cluster, hence generating a novel contracted graph that shrank in size.

Traveling wave solutions of reaction-diffusion systems have been studied to explain wave propagation phenomena in biological organisms.

KAUST Invitational Workshop: Advancements in Data and Artificial Intelligence Tools for Transplantation and General Medicine will bring together clinicians, scientists and experts in data analysis and machine learning/artificial intelligence to showcase their research and modern developments in kidney paired donation.