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.

Despite the significant advancements deep learning models have brought to solving complex problems in the real world, their lack of transparency remains a significant barrier, particularly in deploying them within safety-critical contexts.

Abstract

Neural operator methods provide a novel approach for solving or learning the complex mapp

Neural ODEs have surfaced in the last decade as a new perspective on modelling dynamics by learning the time-derivative that drives the system evolution forward as a neural network.

The robotics industry has manufactured multiple successful robots that are deployed in various domains and have been playing a significant role in the modern economy.

Despite being small and simple structured in comparison to their victims, virus particles have the potential to harm severly and even kill highly developed species such as humans. To face upcoming virus pandemics, detailed quantitative biophysical un- derstanding of intracellular virus replication mechanisms is crucial. Unveiling the relationship of form and function will allow to determine putative attack points relevant for the systematic development of direct antiviral agents (DAA) and potent vacci- nes. Biophysical investigations of spatio-temporal dynamics of intracellular virus replication so far are rare.

This talk will provide a recent topic of the III-nitride-based visible light-emitting diodes (LEDs). The InGaN-based blue LEDs are very contributed to energy-saving for light sources all over the world. Therefore, the 2014 Nobel Prize in Physics was awarded to the inventors of blue LEDs.

Predicting the paths of animals poses a significant challenge, given the intricate nature of their behaviors, the impact of unpredictable environmental elements, individual differences, and the scarcity of precise data on their movements.

Computational imaging systems are based on the joint design of optics and associated image reconstruction algorithms. Of particular interest in recent years has been the development of end-to-end learned “Deep Optics” systems that use differentiable optical simulation in combination with backpropagation to simultaneously learn optical design and deep network post-processing for applications such as hyperspectral imaging, HDR, or extended depth of field. In this talk I will in particular focus on new developments that expand the design space of such systems from simple DOE optics to compound refractive optics and mixtures of different types of optical components.

The traditional trajectory of electronic device scaling, guided by Moore's law, is currently encountering physical limitations. To address this, the "More-than-Moore" (MtM) trend has emerged, emphasizing the diversification of device functionalities to include sensing, storing, and processing data.

As more and more modern time series data sets are becoming high dimensional, the problem of classification in this context has received increasing attention. We propose a statistical framework for classifying multivariate stationary Gaussian time series where the number of covariates, the length of the series, and the sample size, all grow to infinity.