Events

Sep 24 - Sep 30, 2023

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  Creating Cognitive Enterprises Through Decision Intelligence

  Dr. Anas Alfaris, Dr. Ahmad Alabdulkareem

  Sep 25, 11:30 - 12:30

  B9 L2 H2 H2

  Intelmatix artificial intelligence

  It is difficult to predict the future, but ultimately, what matters is creating the future we want to live in. Our past and present are the product of our previous decisions. The decisions we make today will pave the way for the future. It is important that the right decision is made at the right time leading to the right outcome, paving the way towards the desired future.

Sep 17 - Sep 23, 2023

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  Predictive Performance Test based on the Exhaustive Nested Cross-Validation for High-dimensional data

  Iris Ivy Gauran, Postdoctoral Research Fellow, Biostatistics Group

  Sep 21, 12:00 - 13:00

  B9 L2 H2

  Cross-validation is an algorithmic technique extensively used for estimating the prediction error, tuning the regularization parameter, and choosing between competing predictive rules.
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  Breiman’s Samplers or Models? There is a little but important difference: Models, including priors, can be wrong!

  Dr. Yannis Yatracos

  Sep 20, 15:00 - 16:00

  B1 L2 R2202

  Breiman (2001) urged statisticians to provide tools when the data, X=s(θ,Y); sampler s is available as Black-Box, parameter θεΘ, Y is random, either observed or latent. The paper’s discussants, D. R. Cox and B. Efron, looked at the problem as X-prediction, surprisingly neglecting the statistical inference for θ, and disagreed with the main thrust of the paper.
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  Additive Schwarz Methods for Convex Optimization - Theory and Applications

  Jongho Park, Research Scientist, Applied Mathematics and Computational Sciences

  Sep 19, 16:00 - 17:00

  B5 L5 R5209

  Additive Schwarz methods convex optimization

  This talk is devoted to additive Schwarz methods for convex optimization. First, we propose an abstract framework for additive Schwarz methods for convex optimization. The framework's flexibility allows it to handle composite optimization problems and inexact local solvers. Moreover, it establishes a sharp convergence theory that agrees with the classical theory when addressing linear problems.
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  Neuro-symbolic methods for Semantic Web ontologies

  Robert Hoehndorf, Associate Professor, Computer Science

  Sep 18, 11:30 - 12:30

  B9 L2 H2 H2

  Semantic web Ontologies neuro-symbolic methods

  Semantic Web ontologies are widely used to provide a conceptual schema for sharing and integrating data and knowledge using a logic-based language. The content of ontologies may also be used to provide background knowledge in machine learning models or provide domain-specific constraints that can be verified automatically and used for zero-shot predictions. The combination of embedding symbolic representations (such as ontologies) and extracting symbolic representations from the embeddings are two main components of neuro-symbolic AI systems. I will introduce methods for embedding Semantic Web ontologies and outline some of the properties of the embeddings that relate to model and proof theory.
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  Universal light encoders: artificial intelligent optical hardware for real-time hyperspectral imaging and ultrasensitive detection

  Andrea Fratalocchi, Professor, Electrical and Computer Engineering

  Sep 17, 12:00 - 13:00

  B9 L2 H2

  Metasurfaces machine learning

  In this talk, I will review the fundamentals and application of this technology in various areas, ranging from the inverse design of ultra-flat optical components to HyplexTM, an innovative camera for acquiring and processing high-resolution hyperspectral videos in real-time at 30 frames per second.

Sep 10 - Sep 16, 2023

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  Goodness-of-fit tests for multivariate skewed distributions based on the characteristic function

  Maicon J. Karling, Postdoctoral Research Fellow, Statistics

  Sep 14, 12:00 - 13:00

  B9 L2 H2

  Goodness-of-fit tests determine how well a set of observed data fits a particular probability distribution. They can also show if some categorical variable follows a hypothesized family of distributions.
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  Stability and Asymptotics in Electrohydrodynamics

  Nuno J. Alves, Ph.D. Student, Applied Mathematics and Computational Sciences

  Sep 12, 15:00 - 17:00

  B1 L4 R4214

  Relative Energy Analysis relative energy method

  This dissertation focuses on the relative energy analysis of two-species fluids composed of charged particles. In particular, it explores several applications of the relative energy method to Euler-Poisson systems, enabling a comprehensive stability analysis of these systems.
• 

  Spatial Models and Extreme-Value Methods for Wildfire Risk Assessment

  Daniela Cisneros, Ph.D. Student, Statistics

  Sep 11, 16:00 - 17:00

  B3 L5 R5220

  extreme statistics Applied Machine Learning geospatial statistics

  The statistical modeling of spatial and extreme events provides a framework for the development of techniques and models to describe natural phenomena in a variety of environmental, geoscience, and climate science applications. In a changing climate, various natural hazards, such as wildfires, are believed to have evolved in frequency, size, and spatial extent, although regional responses may vary.
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  Towards Cyber Resilient Autonomous Vehicle Ecosystems: Pitfalls and Enablers

  Sep 11, 11:30 - 12:30

  B9 L2 H2 H2

  cybersecurity autonomous vehicles

  Currently, there is a substantial push towards autonomous vehicles in the market. However, fully autonomous vehicles, using extensive fault-tolerance e.g., in x-by-wire functions, are still not quite safe from an accidental faults perspective. Furthermore, they present an even greater threat surface to combined accidental and malicious faults. This pitfall has been very slowly recognized by car makers. The consequences of such accidents or attacks are likely to be severe, life-threatening included. This talk will discuss this threat surface in an analysis including the whole ecosystem.
• 

  Gbps Laser-based underwater wireless optical communication

  Boon Ooi, Professor, Electrical and Computer Engineering

  Sep 10, 12:00 - 13:00

  B9 L2 H2

  underwater optical communications photonics

  I will focus my discussion on optoelectronic devices and system challenges facing long-distance, multiple-Gbps underwater wireless optical communication. The future perspective of underwater photonics will also be discussed.

Sep 3 - Sep 9, 2023

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  From Euler flows with friction to gradient flows and applications

  Athanasios Tzavaras, Professor, Applied Mathematics and Computational Sciences

  Sep 7, 12:00 - 13:00

  B9 L2 H2

  I will review some works on the high-friction limit (or small mass approximation) from Euler flows to advection-diffusion systems that are gradient flows, and related asymptotic problems in fluid mechanics. The formulation exploits the variational structure of compressible Euler flows and is connected to the interpretation of nonlinear Fokker-Planck systems as gradient flows in Wasserstein distance.
• 

  Mean Field Games price formation models

  Julian David Gutierrez Pineda, Ph.D. Student, Applied Mathematics and Computational Sciences

  Sep 6, 14:00 - 16:00

  B1 L4 R4214

  mean-field games Applied Machine Learning

  This thesis studies mean-field games (MFGs) models of price formation. The thesis focuses explicitly on a MFGs price formation model proposed by Gomes and Saúde. The thesis is divided into two parts. The first part examines the deterministic supply case, while the second part extends the model to incorporate a stochastic supply function. We explore different approaches, such as Aubry-Mather theory, to study the properties of the MFGs price formation model and alternative formulations using a convex variational problem with constraints. We propose machine-learning-based numerical methods to approximate the solution of the MFGs price formation model in the deterministic and stochastic setting.
• 

  Universal Portfolio Algorithm and Confidence Sequences

  Francesco Orabona, Associate Professor, Computer Science

  Sep 5, 16:00 - 17:00

  B5 L5 R5209

  Universal Portfolio algorithm

  In this talk, I will describe a surprising application for a known online learning problem and its optimal algorithm.
• 

  Model-Based Computational Cryo-Electron Tomography Joint Reconstruction with Awareness of Noise

  Yuanhao Wang, Ph.D. Student, Electrical and Computer Engineering

  Sep 4, 17:30 - 19:00

  B1 L2 R2202

  Tilt-series cryo-electron tomography (cryo-ET) is an established imaging tech- nique used in several fields like biology and material science. Despite its success, cryo-ET remains an arduous task. The missing-wedge acquisition, the motion, and the high level noise are the main challenges existing in this field. In this dissertation, we tackle these challenges through the exploration of three distinct approaches: plug and play approach, adaptive differentiable density grids and adaptive tensorial density fields representation.
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  Physics-based Simulation in Visual Computing

  Dominik L. Michels, Associate Professor, Computer Science

  Sep 4, 11:30 - 12:30

  B9 L2 H2 H2

  visual computing machine learning physics-based simulation

  This talk covers a selection of previous and current work presenting a broad spectrum of research highlights ranging from simulating stiff phenomena such as the dynamics of fibers and textiles, over liquids containing magnetic particles, to the development of complex ecosystems and weather phenomena. Moreover, connection points to the growing field of machine learning are addressed and an outlook is provided with respect to selected technology transfer activities.
• 

  KAUST Masterclass: Semiconductor Photonics

  Boon Ooi, Professor, Electrical and Computer Engineering

  Sep 3, 13:30 - 17:00

  KAUST

  semiconductor photonics

  Semiconductor Photonics is an immersive master class that explores the cutting-edge field of advanced optoelectronics and its diverse applications. This class brings together experts and enthusiasts to delve into the forefront of research and innovation in semiconductor photonics. Key topics include Semiconductor Laser-Based Lighting and Visible Light Communications, Artificial Intelligent Optoelectronics Hardware for Neuromorphic Computing, Optoelectronic Micro-Devices for Novel AR and VR Displays, and Integrated Silicon Photonics. By covering these varied areas, the class offers a comprehensive understanding of semiconductor photonics and its implications in information technology, healthcare, computing, and beyond.
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  Waves Functional Materials - 2023-09-03

  Ying Wu, Professor, Applied Mathematics and Computational Sciences

  Sep 3, 12:00 - 13:00

  B9 L2 H2

  Wave functional materials Metasurfaces

  I will introduce the development of effective medium and deep learning models, two distinct methods in the design of metamaterials, followed by some illustrative examples demonstrating the intriguing properties, such as redirection, emission rate enhancement, wave steering, and cloaking.

Aug 27 - Sep 2, 2023

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  Efficient Intensity Estimation Using Adaptive Bandwidths on Linear Networks: A Study and Application in Traffic Accident Analysis

  Jonatan A. González, Postdoctoral Research Fellow, Statistics

  Aug 31, 12:00 - 13:00

  B9 L2 H2

  Estimating first-order intensity functions is crucial in the analysis of point patterns on linear networks, but selecting suitable bandwidths for non-parametric methods remains challenging. We propose an adaptive intensity estimator for the heating kernel that adjusts bandwidths based on data points, a novel approach in this context.
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  Finite element approximations for variable-order fractional diffusion problems

  Prof. Wenyu Lei, University of Electronic Science and Technology of China

  Aug 29, 16:00 - 17:00

  B4 L5 R5209

  Finite elements Variable-order fractional diffusion problems

  We propose a finite element scheme for fractional diffusion problems with varying diffusivity and fractional order. A number of challenges are encountered when discretizing such problems. The first is the heterogeneous kernel singularity in the fractional integral operator. The second comes from the dense discrete operator with its quadratic growth in memory footprint and arithmetic operations. To address these challenges, we propose a strategy that decomposes the system matrix into three components. The first is a sparse matrix that handles the singular near-field separately and is computed by adapting singular quadrature techniques available for the homogeneous case to the case of spatially variable order.
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  Thwarting Drones’ Threat

  Roberto Di Pietro, Professor, Computer Science

  Aug 28, 11:30 - 13:00

  B9 L2 H2 H2

  UAVs, or drones, are a dual-use technology that is gaining momentum, with applications spanning from agriculture to warfare. In this talk we will survey some of the threats posed by drones, and will discuss some scientific contributions to the field aimed at providing a way to reduce the risk posed by a rogue use of this technology. We will also highlight some related research directions.
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  Enabling Next Generation Wireless Communication through mm-Wave Reconfigurable Intelligent Surface (RIS)

  Atif Shamim, Program Chair, Electrical and Computer Engineering

  Aug 27, 12:00 - 13:00

  B9 L2 H2

  5G and beyond electromagnetic waves

  The mm-wave 5G and beyond communication systems significantly improve the data rate, user capacity, and latency, however, the electromagnetic (EM) wave propagation suffers from high atmospheric attenuation as compared to the sub-6 GHz bands.

Aug 13 - Aug 19, 2023

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  Tackling the Communication Bottlenecks of Distributed Deep Learning Training Workloads

  Chen-Yu (Elton) Ho, Ph.D. Student, Computer Science

  Aug 14, 08:00 - 10:00

  B2 L5 R5220

  This presentation will focus on addressing the communication bottlenecks in distributed deep learning (DDL) training workloads. Deep neural networks (DNNs) are widely used in various domains, but training them can be time-consuming, especially with large models and datasets. Three innovative solutions are proposed and evaluated in the dissertation.

Jul 30 - Aug 5, 2023

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  Hybrid Analysis and Modeling as an Enabler for Digital Twins

  Prof. Adil Rasheed, Computer Science and Engineering, Norwegian University of Science and Technology

  Aug 3, 11:00 - 12:00

  B1 L4 R4214

  hybrid modelling

  During this talk, we will delve into a new paradigm in modeling, called Hybrid Analysis and Modeling, which has the ability to combine the best of both the physics-driven and data-driven worlds, while eliminating their weaknesses. This approach has shown remarkable utility in the context of digital twin technology and will be demonstrated through a lab-scale experimental setup, mimicking building energy modeling.
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  Simulation of the drawdown and its duration in Lévy models via stick-breaking Gaussian approximation

  Prof. Aleksandar Mijatovic, Statistics, University of Warwick

  Aug 2, 15:00 - 16:00

  B1 L2 R2202

  Abstract In this talk, I will describe a computational method for expected functionals of the drawdown and its duration in exponential Lévy models. It is based on a novel simulation algorithm for the joint law of the state, supremum and time the supremum is attained of the Gaussian approximation for a general Lévy process. We bound the bias for various locally Lipschitz and discontinuous payoffs arising in applications and analyse the computational complexities of the corresponding Monte Carlo and multilevel Monte Carlo estimators. Monte Carlo methods for Lévy processes (using Gaussian

Jul 23 - Jul 29, 2023

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  Model-based and Learned, Inverse Rendering for 3D Scene Reconstruction and View Synthesis

  Rui Li, Ph.D. Student, Electrical and Computer Engineering

  Jul 24, 18:00 - 20:00

  B1 L2 R2202

  Recent advancements in inverse rendering have exhibited promising results for 3D representation, novel view synthesis, scene parameter reconstruction, and direct graphical asset generation and editing.

Jul 16 - Jul 22, 2023

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  Prioritizing Causative Genomic Variants by Integrating Molecular and Functional Annotations from Multiple Biomedical Ontologies

  Azza Althagafi, Ph.D. Student, Computer Science

  Jul 20, 13:00 - 17:00

  B2 L5 R5220

  The dissertation focuses on developing novel computational methods to improve the diagnosis of patients with rare or complex diseases. By systematically relating human phenotypes resulting from gene function loss or change to gene functions and anatomical/cellular locations, the candidate aims to enhance the prediction and prioritization of disease-causing variants. These methods, leveraging graph-based machine learning and biomedical ontologies, demonstrate significant improvements over existing approaches. The presentation will include a systematic evaluation of the methods, demonstrating their ability to compensate for incomplete data and their applications in biomedicine and clinical decision-making. This research contributes to more effective methods for predicting disease-causing variants and advancing precision medicine, offering promising prospects for improved diagnostics and patient care.
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  Ontology design patterns and methods for integrating phenotype ontologies

  Sarah Alghamdi, Ph.D. Student, Computer Science

  Jul 20, 09:00 - 10:00

  B3 L5 R5209.

  bio-ontology phenotype ontologies

  Ontologies are widely used in various domains, including biomedical research, to structure information, represent knowledge, and analyze data. Combining ontologies from different domains is crucial for systematic data analysis and comparison of similar domains. This requires ontology composition, integration, and alignment, which involve creating new classes by reusing classes from different domains, aggregating types of ontologies within the same domain, and finding correspondences between ontologies within the same or similar domain.
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  Towards the Gene Regulation Knowledge Commons

  Prof. Jesualdo Tomas Fernandez Breis, University of Murcia, Spain

  Jul 19, 11:30 - 13:00

  B2 L5 R5220

  Knowledge about transcription factor binding and regulation, target genes, cis-regulatory modules and topologically associating domains is not only defined by functional associations like biological processes or diseases, but also has a determinative genome location aspect.
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  Solutions for Perishables Shelf-life Extension and Spoilage Detection Towards Food Waste Reduction

  Asrar Damdam, Ph.D. Student, Electrical and Computer Engineering

  Jul 16, 16:00 - 18:00

  B2 L5 R5220

  Food loss and waste present a significant challenge to global sustainability, with approximately 1.3 million tonnes of food being lost or wasted each year. This not only leads to the depletion of resources but also contributes to greenhouse gas emissions. This dissertation focuses on the development and implementation of non-invasive solutions to extend the shelf life and monitor the quality of fresh foods, with the ultimate goal of reducing food loss and waste.

Jul 9 - Jul 15, 2023

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  Cybersecure and Resilient Power Systems with Distributed Energy Resources

  Ioannis (Yannis) Zografopoulos, Ph.D. Student, Electrical and Computer Engineering

  Jul 10, 14:00 - 16:00

  B5 L5 R5209

  Power systems constitute a pillar of the critical infrastructure, and, as a result, their cybersecurity is paramount. Traditional power system architectures are moving from their original centralized nature to a distributed paradigm. This transition has been propelled by the rapid penetration of distributed energy resources (DERs) such as rooftop solar panels, battery storage, etc. However, with the introduction of new DER devices, the threat surface of power systems is inadvertently expanding.

Jul 2 - Jul 8, 2023

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  Low-Power Edge-Enabled Sensor Platforms

  Jose Ilton De Oliveira Filho, Postdoctoral Research Fellow, Electrical and Computer Engineering

  Jul 6, 15:00 - 17:00

  B4 L5 R5220

  On-site sensing systems provide fast and timely information about a myriad of applications ranging from chemical and biological to physical phenomena in the environment or the human body. Such systems are embedded in our daily life for detecting pollutants, monitoring health, and diagnosing diseases. This dissertation focuses on the design, development, and implementation of miniaturized PoC devices for achieving high sensitivity, selectivity, and reliability through a combination of hardware and software strategies at the edge.
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  Axisymmetric flows with swirl for Euler and Navier-Stokes equations

  Ioanna Mousikou, Ph.D. Student, Applied Mathematics and Computational Sciences

  Jul 6, 15:00 - 16:00

  B1 L4 R4214

  We consider the incompressible axisymmetric Navier-Stokes equations as an idealized model of tornado-like flows. Assuming that an infinite vortex line that interacts with a boundary surface resembles the tornado core, we look for stationary self-similar solutions of the axisymmetric Euler and the axisymmetric Navier-Stokes equations emphasizing the connection among them as the viscosity ν → 0.

Jun 4 - Jun 10, 2023

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  Ultrawide Bandgap Semiconductors Modeling, Epitaxy, Processing, and Applications for Ultraviolet Emission and Detection

  Yi Lu, Ph.D. Student, Electrical and Computer Engineering

  Jun 8, 14:30 - 16:30

  B5 L5 R5209

  Wide bandgap III-Nitride semiconductor blue light-emitting diodes (LED) development has spawned the prestigious Nobel Prize in Physics in 2014. Building upon this success, the scope of research has expanded to ultrawide bandgap semiconductors, which possess immense potential in the realm of ultraviolet (UV) photonics. These materials have gained attention for their applicability in various areas, such as public sterilization, solar-blind UV communication, and real-time UV monitoring.
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  Exploiting Data Sparsity in Matrix Algorithms for Adaptive Optics and Seismic Imaging

  Yuxi Hong, Ph.D. Student, Computer Science

  Jun 7, 14:30 - 17:00

  B2 R5209;

  Matrix Algorithms machine learning High Performance Computing

  This thesis addresses the exponential growth of experimental data and the resulting computational complexity seen in two major scientific applications, which account for most cycles consumed on today's supercomputers.
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  Preconditioning for Kernel Matrices

  Edmond Chow, Professor and Associate Chair, School of Computational Science and Engineering, Georgia Institute of Technology

  Jun 6, 16:00 - 17:00

  B2 L5 R5220

  Preconditioning Kernel Matrices

  Coffee Time: 15:30 - 16:00. Kernel matrices can be found in computational physics, chemistry, statistics, and machine learning. Fast algorithms for matrix-vector multiplication for kernel matrices have been developed, and is a subject of continuing interest, including here at KAUST. One also often needs fast algorithms to solve systems of equations involving large kernel matrices. Fast direct methods can sometimes be used, for example, when the physical problem is 2-dimensional. In this talk, we address preconditioning for the iterative solution of kernel matrix systems. The spectrum of a kernel matrix significantly depends on the parameters of the kernel function used to define the kernel matrix, e.g., a length scale.
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  Panel Discussion: A Roadmap for Bringing to Earth the Energy of the Stars

  Prof. Amitava Bhattacharjee, Dr. Michael Zarnstorff, Dr. Spencer Pitcher, Mr. Richard Carty

  Jun 5, 16:15 - 16:45

  B20 Auditorium

  roadmap Earth energy Stars

  “Practical fusion power is thirty years off and always will be.” Most people are instantly enraptured at the thought that a single glass of water will provide enough fusion fuel for their lifetime – if only it could be safely unlocked – and most of us have heard, as well, that we should not set our watches for when this will occur.
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  Lecture 2: The Coming Dawn of Commercial Fusion Energy

  Prof. Amitava Bhattacharjee, Astrophysical Sciences, Princeton University

  Jun 5, 15:45 - 16:15

  B20 Auditorium

  Generating fusion power from a stellarator is an exciting scientific and engineering challenge, one that promises to produce a stable, green, and nearly perennial energy source for mankind. To do so economically and safely will require a combination of several interdisciplinary breakthroughs including extreme scale computing (with adroit use of artificial intelligence and machine learning), high-temperature superconductors, materials science research, and robotics, to name a few. Overcoming this challenge will need a worldwide effort, involving academia, national laboratories, and industry, and contributions from diverse economies and people.
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  A Fusion Fest at KAUST

  Prof. Amitava Bhattacharjee, Astrophysical Sciences, Princeton University

  Jun 5, 14:30 - 16:45

  B20 Auditorium

  KAUST’s Extreme Computing Research Center and Program in Applied Physics are sponsoring an immersive Fusion Fest on the afternoon of June 5, 2023. The program, which is designed to attract KAUST talent to address key challenges in bringing about commercially profitable “green” fusion power, consists of two live lectures by Professor Amitava Bhattacharjee and a hybrid live-virtual panel discussion (with Q&A) on a pathway to the realization of a commercial stellarator, led by physicist Bhattacharjee, with participation from engineering experts from ITER and the Princeton Plasma Physics Laboratory and a veteran financial advisor to the energy industry.
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  Lecture 1: Hidden Symmetries and Fusion Energy

  Prof. Amitava Bhattacharjee, Astrophysical Sciences, Princeton University

  Jun 5, 14:30 - 15:30

  B20 Auditorium

  Hidden Symmetries Fusion energy

  The most compelling transformational use of magnetically confined, high-temperature plasma is to realize sustained fusion energy. The tokamak, which is the leading magnetic confinement concept in the world today, first realized in 1958, has the geometry of a torus and toroidal symmetry, giving it good confinement properties. Nevertheless, the tokamak has a number of unresolved stability issues related to its current-carrying plasma that may be obstacles to its ultimate success. In contrast, in the stellarator, the confining magnetic field is mostly produced by external current-carrying coils.
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  Approximate Bayesian inference based on dense matrices and new features using INLA

  Jun 4, 15:00 - 16:00

  B4 L5 R5220

  Bayesian computational statistics

  The Integrated Nested Laplace Approximations (INLA) method has become a commonly used tool for researchers and practitioners to perform approximate Bayesian inference for various fields of applications. It has become essential to incorporate more complex models and expand the method’s capabilities with more features. In this dissertation, we contribute to the INLA method in different aspects.
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  New Frontiers in Space Laser Communications aiming for Beyond 5G/6G

  Morio Toyoshima, Director General of Wireless Networks Research Center, National Institute of Information and Communications Technology

  Jun 4, 14:30 - 15:30

  B1 L3 R3119

  communication engineering

  These discussions on the way how information and communication technology (ICT) should be in Beyond 5G (B5G) and 6G are accelerating, the space laser communication is becoming more advanced and active in the field of space communications.
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  Contemporary Techniques in the Emerging Free Space Optical Communication Systems

  Dmitrii Briantcev, Ph.D., Electrical and Computer Engineering

  Jun 4, 12:30 - 14:00

  B1 L3 R3119

  optical communication technology Free Space Optics Conditional Generative Adversarial Network Recurrent Neural Network

  The goal of this work is to investigate and advance a research on various topics, vital for the development of the future generations of optical communication technology. In the first part of the work, we present a fast and efficient simulation method of structured light free space optics (FSO) channel effects from propagation through the turbulent atmosphere.
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  Stochastic environmental modeling in a time of convergence: physics meets artificial intelligence

  Prof. Stefano Castruccio, Associate professor, University of Notre Dame, USA

  Jun 4, 10:00 - 11:00

  B1 L4 R4102

  It is widely acknowledged how the relentless surge of Volume, Velocity and Variety of data, as well as the simultaneous increase of computational resources have stimulated the development of data-driven methods with unprecedented flexibility and predictive power. However, not every environmental study entails a large data set: many applications ranging from astronomy or paleo-climatology have a high associated sampling cost and are instead constrained by physics-informed partial differential equations. Throughout the past few years, a new and powerful paradigm has emerged in the machine learning literature, merging data-driven and physics-informed problems, hence providing a unified framework for a whole spectrum of problems ranging from data-rich/context-poor to data-poor/context-rich. In this talk, I will present this new framework and discuss some of the most recent efforts to reformulate it as a stochastic model-based approach, thereby allowing calibrated uncertainty quantification.

May 28 - Jun 3, 2023

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  Leave-Group-Out Cross-Validation for Latent Gaussian Models

  Zhedong Liu, Ph.D. Student, Statistics

  May 30, 15:30 - 17:30

  B1 R4102;

  The commonly used leave-one-out and K-fold cross-validation methods are not suitable for structured models with multiple prediction tasks. To overcome this limitation, we introduce leave-group-out cross-validation, which allows groups to adapt to different tasks. We propose an automatic group construction method and provide an efficient approximation for latent Gaussian models. Moreover, this method is conveniently implemented in the R-INLA software.
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  Criticism and robustification of latent Gaussian models

  Rafael Medeiros Cabral, Ph.D. Student, Statistics

  May 28, 15:00 - 16:00

  B1 L4 R4102

  latent Gaussian models

  Latent Gaussian models (LGM) are widely used but struggle with certain datasets that contain non-Gaussian features, such as sudden jumps or spikes. This dissertation aims to provide tools for researchers to check the adequacy of the fitted LGM (criticism); if the check fails, offer efficient and user-friendly implementations of latent non-Gaussian models, which lead to more robust inferences (robustification).

May 21 - May 27, 2023

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  Assessing DNS Resilience and Robustness

  Prof. Fatemah Alharbi, Assistant Professor, the Computer Science Department, Taibah University, Yanbu, KSA.

  May 25, 15:30 - 16:30

  B4 L5 R5220.

  resilience Robustness

  The Domain Name System (DNS) is a core protocol for the Internet. It resolves mappings between Internet Protocol (IP) addresses and their corresponding Fully Qualified Domain Names (FQDNs). Since all Internet communications rely on it, DNS structuring should therefore be resilient and robust against failure to avoid any service interruption. While the research community and experienced practitioners have established best practices to this end, many worldwide DNS implementations are still prone to many types of configuration errors. In this talk, I discuss the adoption of these approaches in some countries. Also, a case study is presented considering domains in Saudi Arabia (.sa) that illustrates the value of measuring the DNS at this scale. The results are valuable to improve the DNS infrastructure in the kingdom. Lastly, I provide recommendations to improve DNS service resilience and robustness.
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  Next Generation Multiple Access with Cell-Free Massive MIMO

  Michalis Matthaiou, Professor, Electrical Engineering and Computer Science, Queen’s University Belfast, United Kingdom

  May 24, 11:00 - 12:00

  B1 L3 R3119

  Massive MIMO Systems Adaptive communication systems

  This talk provides an overview of current research efforts on the CF-mMIMO systems and their promising future application scenarios.
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  Towards The Implementation of Reliable Hyperloop Communication Systems

  Wafa Hedhly, Ph.D., Electrical and Computer Engineering

  May 24, 09:00 - 10:45

  B1 L3 R3119

  communications hyperloop communication systems

  This work is geared toward the design and analysis of reliable Hyperloop communication systems. Hyperloops pose several challenges for the communication system for providing a reliable communication system while dealing with a complex structure, moving at tremendous speeds inside an evacuated tube. The adopted sealed steel-structured tube in the Hyperloop prevents signal penetration, isolating the inner world from the outside and creating a highly scattering environment for electromagnetic (EM) waves. Furthermore, the exceptionally high speed of the traveling pod results in severe Doppler shifts and frequent handovers, leading to increased transmission errors and delays. We propose a novel system configuration where wireless transmitters or access points (APs) are placed inside the tube to communicate with the moving receiver using optical fiber as a backhaul link.
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  Multiscale numerical methods. Theory and Practice

  Yalchin Efendiev, Professor, Institute for Scientific Computation, Texas A and M University

  May 21, 12:00 - 13:00

  B9 L3 R3128

  numerical methods

  In this talk, I will talk about general approaches for multiscale modeling (closely related to porous media applications). I will mainly focus on numerical approaches, where multiscale finite element basis functions are constructed and used in approximating the solution. In these approaches, macroscopic equations are formed via some variational formulations of the problem. I will discuss how these approaches are used in deriving, so called upscaling techniques and the relation to well known upscaling methods. The concepts discussed in the talk are used for linear and nonlinear problems. I will discuss some applications.