This talk will give an overview of the research of the High-Performance Visualization research group (vccvisualization.org) at KAUST.

The important influence of Internet and Communication Technology (ICT) has reshaped society, touching every aspect from the economy to healthcare. As the widespread deployment of 5G continues, there is an on-going focus on the inception of the sixth generation (6G) of wireless communication systems (WCSs). Anticipated to shape the future of connectivity in the 2030s, 6G aims to deliver unparalleled communication services to meet the demands of hyper-connectivity.

Traditional Topological Data Analysis (TDA) methods, such as Persistent Homology (PH), rely on distance measures (e.g., cross-correlation, partial correlation, coherence, and partial coherence) that are symmetric by definition. While useful for studying topological patterns in functional brain connectivity, the main limitation of these methods is their inability to capture the directional dynamics - which is crucial for understanding effective brain connectivity.

We consider the problem of how efficiently shallow neural networks with the ReLUk activation function and 𝑁 neurons can approximate functions from Sobolev spaces 𝑊𝑠(𝐿𝑝(Ω)) with error measured in the 𝐿𝑞(Ω)-norm.

Autonomous driving technology promises to revolutionize transportation systems, yet faces significant challenges in 3D perception, real-time computing, and decision-making in complex, dynamic environments.

Free boundary problems emerge naturally in mathematical models representing physical, biological, or financial phenomena, such as ice melting, population dynamics, or stock market behavior. These problems involve solving partial differential equations for both an unknown function and an unknown domain. This talk will explore several free boundary problems and different methods to address them.

We introduce a parallel hybrid approach for Bayesian inference of large spatio-temporal Gaussian processes, combining domain decomposition with the Rao-Blackwellized Monte Carlo estimator. This method enhances speed and scalability by integrating iterative Krylov methods with direct factorizations, improving accuracy and robustness in large-scale datasets.

Abstract

Searchable encryption has been a hot topic over the past two decades.

In the realm of fast and scalable approximated Bayesian Inference, two highly sought-after approaches have traditionally been the Laplace Method and Variational Bayes.

With the advent of wearable sensors and internet of things (IoT), there is a new focus on electronics which can be bent so that they can be worn or mounted on non-planar objects.

Second-order partial differential equations (PDEs) are traditionally classified as being parabolic, elliptic, or hyperbolic in nature, and this classification largely determines the kind of analytical and numerical techniques that can be successfully applied to them.

This talk is devoted to the finite element approximation of boundary value problems with interfaces. We shall consider a simple second order elliptic equation with discontinuous coefficients and present the most common approaches for its finite element discretization.

Currently, attention mechanism becomes a standard fixture in most state-of-the-art NLP, Vision and GNN models, not only due to outstanding performance it could gain, but also due to plausible innate explanation for the behaviors of neural architectures it provides, which is notoriously difficult to analyze.

In a society progressively increasingly welcoming automation, robotics constitutes a very active field of research. This presentation will feature recent robotic-related activities taking place at KAUST, ranging from abstract and analytical to concrete and experimental.

The design of efficient parallel/distributed optimization methods and tight analysis of their theoretical properties are important research endeavors. While minimax complexities are known for sequential optimization methods, the theory of parallel optimization methods is surprisingly much less explored, especially in the presence of data, compute and communication heterogeneity.

Deep Neural Networks (DNNs) have demonstrated exceptional performance in various fields, but they often suffer from performance degradation when encountering domain shifts, common in real-world applications. This thesis addresses this challenge by exploring and improving robustness approaches.

The design of efficient parallel/distributed optimization methods and tight analysis of their theoretical properties are important research endeavors. While minimax complexities are known for sequential optimization methods, the theory of parallel optimization methods is surprisingly much less explored, especially in the presence of data, compute and communication heterogeneity.

This seminar examines the transformative impact of wearable sensor technologies across healthcare, environmental monitoring, and marine biology. In healthcare, these sensors enable continuous monitoring for enhanced patient care and disease management.

Large language models (LLM) have become part of our daily life and are now indispensable tools for conducting research as well. The performance of LLMs is known to increase as the model size and data size is scaled up.

Lattice models are widely used to study various statistical and quantum physics phenomena. For instance, to model fluid percolation, one takes a part of a honeycomb lattice inside a fixed rectangle and a random coloring of the hexagons into two colors, blue and yellow. A typical question is then to find the probability of a blue path between opposite sides of the rectangle as the hexagons become smaller and smaller.

Over the past three decades, high-performance computing has undergone significant transformations. Tremendous advances were made in terms of the compute capability per node, memory bandwidth, as well as high-performance interconnect and storage. However, these technologies have evolved at disparate rates.

During the three sessions, we give a general overview of the Wolfram technology stack, which includes Wolfram Mathematica and its underlying language, the Wolfram language. Mathematica was created in 1988 by Stephen Wolfram and it has maintained compatibility.

The overarching goal of the KAUST Computational Sciences Group is enabling accurate and efficient physics-based simulations for applications in Visual Computing. Towards this goal, the group develops new principled computational methods based on solid theoretical foundations.

(Ultra)wide bandgap compound semiconductors including AlN, GaN, Ga2O3 and In2O3 have attracted enormous interests. They offer markedly larger figures of merits for power and RF applications than other known semiconductors.

During the three sessions, we give a general overview of the Wolfram technology stack, which includes Wolfram Mathematica and its underlying language, the Wolfram language. Mathematica was created in 1988 by Stephen Wolfram and has maintained compatibility.