Si photonics has been developed to enable the next generation tele- and data-communications for its high performance, making use of the mature silicon CMOS technologies.

The Euler-Poisson equations give the classical model of a self-gravitating star under Newtonian gravity. It is widely expected that, in certain regimes, initially smooth initial data may give rise to blow-up solutions, corresponding to the collapse of a star under its own gravity. In this talk, I will present recent work with Yan Guo, Mahir Hadzic and Juhi Jang that demonstrates the existence of smooth, radially symmetric, self-similar blow-up solutions for this problem. I will also comment on the stability of the obtained solution. At the heart of the analysis is the presence of a sonic point, a singularity in the self-similar model that poses serious analytical challenges in the search for a smooth solution.

Abstract

The increased availability of biomedical data, particularly in the public domain, offers

Although technical challenges are still daunting, the clinical utility of neuroprosthetics has increased dramatically over the past few years. This has been accomplished through the convergence of numerous disciplines, which have individually added fundamental understanding/capabilities to systems that interface with the human body to restore senses and movement, or treat prevalent diseases that have currently no foreseeable cure. Among these, predictive multiscale computational modeling methods have greatly aided in the design of neuroprosthetics by embracing the complexity of the nervous system, which span multiple spatial scales, temporal scales, and disciplines. In this talk, we will cover some of the recent advances in bioelectromagnetic systems for healthcare, with a particular focus on visual and hippocampal prosthesis, peripheral neuroprosthetics, and sensors.

The Mat\'ern family of covariance functions is currently the most commonly used for the analysis of geostatistical data due to its ability to describe different smoothness behaviors. Yet, in many applications the smoothness parameter is set at an arbitrary value.

In this talk we will present rigorous analytical results concerning global regularity, in the viscous case, and finite-time singularity, in the inviscid case, for oceanic and atmospheric dynamics models. Moreover, we will also provide a rigorous justification of the derivation of the Primitive Equations of planetary scale oceanic dynamics from the three-dimensional Navier-Stokes equations as the vanishing limit of the small aspect ratio of the depth to horizontal width.

This brief course will cover several algebraic structures, beginning with groups and culminating with algebras. Our primary the focus will be on Lie algebras, and we will introduce the essential properties and results necessary to comprehend the structure of Lie algebras and other related structures

In this talk I will discuss the shift towards hardware acceleration and show with several examples from industry and from research the large role that FPGAs are playing. I will hypothesize that we are in a new era where most of the established assumptions, rules of thumb, and accumulated wisdom about many aspects of computation in general and of data processing in particular no longer hold and need to be revisited.

We study theoretical problems of fault diagnosis in circuits and switching networks, which are among the most fundamental models for computing Boolean functions. We investigate two main cases: when the scheme (circuit or switching network) has the same mode of operation for both calculation and diagnostics, and when the scheme has two modes of operation -normal for calculation and special for diagnostics.

This brief course will cover several algebraic structures, beginning with groups and culminating with algebras. Our primary the focus will be on Lie algebras, and we will introduce the essential properties and results necessary to comprehend the structure of Lie algebras and other related structures.

The “KAUST Workshop on Applied Geometry and Visual Computing” brings together leading scientists from Europe and the United States, presenting their latest results in - Applied and Discrete Differential Geometry - Geometry Processing - Computational Fabrication The talks are related to various problems in Applied Mathematics in general and to further areas of Visual Computing such as Computer Graphics, Physical Simulation and Scientific Visualization. The workshop provides a great opportunity to learn about latest developments and to discuss ongoing work with top researchers in the field.

Gene therapy, particularly, genome editing therapy can potentially be used to treat many genetic diseases including some currently without a cure.

In this talk, Professor Pramod Bhatotia will give an overview of systems research at TU Munich. He will cover his teaching and ongoing research projects. And will conclude the talk with a brief overview of his ERC project.

I will give an overview of recent results for models of collective behavior governed by functional differential equations. The talk will focus on models of interacting agents with applications in biology (flocking, swarming), social sciences (opinion formation) and engineering (swarm robotics), where latency (delay) plays a significant role. I will explain that there are two main sources of delay - inter-agent communications and information processing

Large-scale MIMO communication (at sub-6GHz, mmWave, and sub-THz bands) is a key enabler for 5G, 6G, and beyond. Scaling MIMO systems, however, is subject to critical challenges, such as the large channel acquisition and beam training overhead and the sensitivity to channel estimation errors (especially at lower frequencies) and blockages (at higher frequencies). These challenges make it difficult for MIMO systems to support applications that have high mobility and strict reliability constraints. In this talk, I will first motivate the use of machine learning and sensory data to address these challenges. Then, I will present a few key machine-learning roles, enabling datasets, and recent hardware proof-of-concept prototypes that demonstrate the machine-learning gains in real-world environments.

I will present a data science pipeline which starts with Earth observation data arriving in the ground segment of a satellite mission and ends with a complete user application. I will first briefly present all the tools my group has been developing since 2010 for supporting the various stages of the pipeline. Then, I will concentrate on the recently developed system Strabo 2 which can store big geospatial data encoded in RDF and query them using the Open Geospatial Consortium standard GeoSPARQL. Strabo 2 is the only parallel and distributed RDF store available today that can manage terabytes of geospatial data efficiently.

The impressive success of Deep Learning (DL) in predictive performance tasks has fueled the hopes that this can help addressing societal challenges by supporting sound decision making. However, many open questions remain about their suitability to hold up to this promise. In this talk, I will discuss some of the current limitations of DL, which directly affect their wide adoption. I will focus in particular on the poor ability of DL models to quantify uncertainty in predictions, and I will present Bayesian DL as an attractive approach combining the flexibility of DL with probabilistic reasoning. I will then discuss the challenges associated with carrying out inference and specifying sensible priors for DL models. After presenting a few of my contributions to address these problems, I will conclude by presenting some interesting emerging research trends and open problems which define my current research agenda.

The pressure to achieve carbon-free grids in the near future is driving the rapid integration of renewable energy sources into the grid.

In this talk I will present a new structure preserving, second order in time relaxation-type scheme for approximating solutions of the Schrodinger-Poisson system. We use the Crank-Nicolson scheme as a time-stepping mechanism, whilst the nonlinearity is handled by means of a relaxation approach. For the spatial discretisation we use the standard conforming finite element scheme. The resulting scheme is explicit with respect to the nonlinearity, i.e. it requires the solution of a linear system for each time-step, and satisfies discrete versions of the system’s mass conservation and energy balance laws for constant discretization parameters.

Refined characterizations of the probabilistic behavior of a stationary time-series by focusing on re normalized Markov processes that are conditioned to attain an extreme event, subject to the level of the extremity tending to the upper end point of the marginal distribution

Propagation of acoustic waves in time-varying and/or moving media has attracted a lot of attentions and is expected to lead to many intriguing applications. In this talk, I will discuss our recent work on acoustic wave propagation in spinning media (air or water). I will start with a review of the theoretical foundation built upon the Mie scattering framework, in which both the wave equation and the boundary conditions will be specifically discussed. The study is limited in the linear regime and exhibit the peculiar scattering features.

Theoretical and practical aspects associated with the limiting distributions of block-maxima and peaks-over-threshold events in the case of stationary time-series data. Special emphasis will be placed on the extremal index, a key measure of extremal dependence that allows us to quantify the degree of clustering at the tail of a time-series.

Computational imaging systems are based on the joint design of optics and associated image reconstruction algorithms.

Inevitably, the projection of most graph structures on two-dimensional screens will create errors and therefore visually wrong impressions. In the past, two types of methods have been developed to minimize projection errors and distribute them in a visually pleasing way. The first group of methods, force-directed layouts, interpret the links of a graph as physical springs, while stress-based methods minimize an energy function, which aims to map graph distances faithfully.

Visible VCSELs have been attracting researchers' interest for potential applications as light sources for optical storage, laser printers, projectors, displays, solid-state lighting, optical communications, biosensors, and so on.