The overarching goal of Prof. Michels' Computational Sciences Group within KAUST's Visual Computing Center is enabling accurate and efficient simulations for applications in Scientific and Visual Computing. Towards this goal, the group develops new principled computational methods based on solid theoretical foundations.

Abstract

For over 30 years, Bayesian image analysis has provided an imp

Assessing the effectiveness of cancer treatments in clinical trials raises multiple methodological challenges that need to be properly addressed in order to produce a reliable estimate of treatment effects.

We consider the problem of parameter estimation for a McKean stochastic differential equation, and the associated system of weakly interacting particles. The problem is motivated by many applications in areas such as neuroscience, social sciences (opinion dynamics, cooperative behaviours), financial mathematics, statistical physics. We will first survey some model properties related to propagation of chaos and ergodicity and then move on to discuss the problem of parameter estimation both in offline and on-line settings. In the on-line case, we propose an online estimator, which evolves according to a continuous-time stochastic gradient descent algorithm on the asymptotic log-likelihood of the interacting particle system. The talk will present our convergence results and then show some numerical results for two examples, a linear mean field model and a stochastic opinion dynamics model. This is joint work with Louis Sharrock, Panos Parpas and Greg Pavliotis. Preprint: https://arxiv.org/abs/2106.13751

I will give a self-contained introduction to the theory of the neural network function class and its application to image classification and numerical solution of partial differential equations.

I will give a self-contained introduction to the theory of the neural network function class and its application to image classification and numerical solution of partial differential equations.

Hardware impairments (HWIs) impose a huge challenge on modern wireless communication systems owing to the characteristics like compactness, least complexity, cost effectiveness and high energy efficiency. Numerous techniques are implemented to minimize the detrimental effects of these HWIs ,however, the residual HWIs may still appear as an additive distortion, multiplicative interference, or an aggregate of both. Numerous studies have commenced efforts to model one or the other forms of hardware impairments in the radio frequency (RF) transceivers. Many presented the widely linear model for in-phase and quadrature imbalance (IQI) but failed to recognize the impropriety induced in the system because of the self-interfering signals. Therefore, we have presented not only a rigorous aggregate impairment model along with its complete impropriety statistical characterization but also the appropriate performance analysis to quantify their degradation effects. Latest advances have endorsed the superiority of incorporating more generalized impropriety phenomenon as opposed to conventional propriety.

This talk is devoted to the study of a monetary model proposed by Rotemberg (Journal of Political Economy, 1984). Rotemberg’s model provided a general dynamic structure for investigating government intervention in the open market operation and it has inspired the development of many other models in related fields. This talk concerns a very basic theoretical question on the model, namely the existence and the uniqueness of the equilibrium, which has been an open problem since the publication of Rotemberg’s original paper. This question was partially addressed by Rotemberg by analyzing the linearization of the equation which governs the equilibrium and obtaining a numerical solution around the steady state equilibrium. 

A traditional goal of algorithmic optimality, squeezing out operations, has been superseded because of evolution in architecture. Algorithms must now squeeze memory, data transfers, and synchronizations, while extra operations on locally cached data cost relatively little time or energy. Hierarchically low-rank matrices realize a rarely achieved combination of optimal storage complexity and high-computational intensity in approximating a wide class of formally dense operators that arise in exascale applications.

I will give a self-contained introduction to the theory of the neural network function class and its application to image classification and numerical solution of partial differential equations.

Signal processing and communication communities have witnessed the rise of many exciting communication technologies in recent years. Notable examples include alternative waveforms, massive multiple-input multiple-output (MIMO) signaling, non-orthogonal multiple access (NOMA), joint communications and sensing, sparse vector coding, index modulation, and so on.

We develop a data-driven methodology based on parametric Itô's Stochastic Differential Equations (SDEs) to capture forecast errors' asymmetric dynamics, including the forecast's uncertainty at time zero.

In this thesis, we discuss some new developments in optimization inspired by the needs and practice of machine learning, federated learning, and data science. In particular, we consider seven key challenges of mathematical optimization that are relevant to modern machine learning applications, and develop a solution to each.

About 12 years ago, Bitcoin was created as the first form of decentralized money, with some of the properties of Nash's ideal money. The protocol proposes a novel probabilistic consensus mechanism, that has the potential to automatize and decentralize many other human activities. The Bitcoin network also provides the first decentralized clock, and has a rich statistical physics interpretation. We will explore the foundations of "Decentralization Theory" and explore what can be expected as future developments.

This talk will provide an overview of the III-nitride-based visible light-emitting diodes (LEDs). Especially, the InGaN-based blue LEDs are very contributed to energy-saving for light sources all over the world.

The rapid penetration of renewable energy sources (RESs) into power systems has introduced many challenges in securing a stable network operation and has forced power systems to operate in low inertia conditions. Low short-circuit power and intrinsic inertial response from converter-interfaced RES may cause poor dynamic performance of systems and render the system frequency more vulnerable than conventional grids. The talk proposes and analyzes different strategies using synchronous condenser (SC), synthetic inertia (SI) of wind power plant, and their combination to enhance the frequency stability of low inertia systems under various scenarios and wind conditions.

Despite the recent advances in big data processing, enabled by the emergence of large-scale machine learning techniques, several statistical questions regarding the behavior in the regime of high dimensions of well-established and fundamental methods have remained unresolved.

Often in the context of data centric science and engineering applications, one endeavours to learn complex systems in order to make more informed predictions and high stakes decisions under uncertainty. Some key challenges which must be met in this context are robustness, generalizability, and interpretability.

Simulation tools are often enablers of cutting-edge research in many fields of science and engineering. This also applies to the field of electromagnetics: Design and characterization of many electromagnetic systems and devices, which drive technological advances in areas such as communications, computing, biomedicine, and solar energy, would not be possible without simulation tools. Having said that, developing numerical methods for electromagnetic analysis of such complex systems is not a trivial task. Dimensions of these systems are large (longer than several wavelengths), their frequency of operation has a wide range, and their geometry has sub-wavelength features. When it comes to simulating these systems by solving the Maxwell equations (in differential or integral form), these characteristics translate into multiscale discretizations, large and often ill-conditioned matrix systems with millions of unknowns to be solved for, and long execution times. My research group at KAUST has been developing efficient, accurate, and robust electromagnetic solvers to address these challenges. In this talk, I will provide an overview of my group’s recent research activities. Technical part of my presentation will focus on two examples, where I talk about solvers we have developed to efficiently and accurately simulate photoconductive antennas (used in terahertz source generation) and plasmonic structures (with a wide range of applications from sensing to solar energy). I will provide results that demonstrate the benefits of these solvers over existing methods. I will conclude my talk with a brief description of my future research plans and a few slides about my research supervision, teaching activities, and international visibility of my research group.

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 transformation of critical infrastructures into cyber-physical systems contributes towards modernization allowing for better planning, more flexible control, system-wide optimization, etc. The security, however, of such systems presents significant challenges in controlling and maintaining secure access to critical system resources and services.

Nowadays, the development of portable and wearable electronics becomes unprecedentedly significant because they allow for real-time communication, remote controlling, and monitoring of various potential environmental hazards regardless of location and time.

Biological data sets, such as gene expressions, copy number alteration, and pharmacogenomics, are often high-dimensional and thus difficult to analyze and interpret. This talk presents two data analysis methodologies that we recently developed based on network analysis via Wasserstein optimal transport combined with unsupervised classification techniques.

Classical imaging systems are characterized by the independent design of optics, sensors, and image processing algorithms. In contrast, computational imaging systems are based on a joint design of two or more of these components, which allows for greater flexibility of the type of captured information beyond classical 2D photos, as well as for new form factors and domain-specific imaging systems. In this talk, I will describe how numerical optimization and learning-based methods can be used to achieve truly end-to-end optimized imaging systems that outperform classical solutions.

In a nutshell, Resilient Computing is a new paradigm based on modelling, architecting and designing computer systems so that: they have built-in baseline defences; such defences cope with virtually any quality of threat, be it accidental faults, design errors, cyber-attacks, or unexpected operating conditions; provide incremental protection of, and automatically adapt to, a dynamic range of threat severity; provide sustainable operation. Resilient Computing will be a game changer in the craft of designing computer systems of today and future.

Nowadays acoustic sensors collect huge amount of data on a regular basis to monitor various activities. This necessitates automated audio event recognition using acoustic data. In this talk, we will present effective methods for detecting and classifying different audio events. Interesting ideas and results will be shown for the proposed methods related to biological/bioacoustic signals.