Events

Dec 4 - Dec 10, 2022

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  Characterization of Natural Bacterial Pathogens with Genomic and Predictive Modelling

  Dr. Danesh Moradigaravand, Infectious Disease Epidemiology lab, BESE, KAUST

  Dec 5, 12:00 - 13:00

  B3 L5 R5209

  machine learning Metagenomics

  In this talk, I will first present how the application of phylogenetic and phylodynamic methods to whole genome sequencing data of multidrug resistant bacterial pathogens provided an in-depth understanding of the epidemiology and evolution of these strains on epidemiological time scales. I will then discuss the characterization of the genomic repertoire of bacterial traits using a combination of machine learning, whole genome sequencing and large-scale phenotypic assays. I will then present the leverage of predictive modelling to predict bacterial features, e.g. antimicrobial resistance, growth, and horizontal gene transfer, from genomic biomarkers. I will finally discuss how large-scale phenotypic assays enabled us to identity genes underlying morphogenesis and biofilm formation.
• 

  High Efficiency Photovoltaic Cells and their Applications to Green Hydrogen Production

  Prof. Yoshiaki Nakano, Electrical Engineering and Information Systems, The University of Tokyo

  Dec 4, 14:30 - 15:30

  B4 L5 R5209

  III - V compound semiconductors photovoltaics

  This lecture will review research trends of III-V compound semiconductor ultra-high efficiency photovoltaic (PV) cells based on multiple junctions and quantum well/dot approaches. It will also introduce field trials of highly efficient green hydrogen production through the direct connection of electrolyzers and concentrator PV modules with the above-mentioned compound semiconductor solar cells built-in.
• 

  The Internet of Bodies: A Gateway to Holistic Healthcare - 2022-12-04

  Ahmed Eltawil, Associate Dean for Research, Computer, Electrical and Mathematical Sciences and Engineering

  Dec 4, 12:00 - 13:00

  B9 L2 R2322 H1

  The Internet of Bodies

  The Internet of Bodies (IoBs) is an imminent extension to the vast Internet of Things domain, where wearable, ingestible, injectable, and implantable smart objects form a network in, on, and around the human body.

Nov 27 - Dec 3, 2022

• 

  Generic Regularity in Obstacle Problems

  Professor Alessio Figalli, ETH Zurich

  Nov 29, 16:00 - 17:00

  KAUST

  The classical obstacle problem consists of finding the equilibrium position of an elastic membrane whose boundary is held fixed and which is constrained to lie above a given obstacle. By classical results of Caffarelli, the free boundary is smooth outside a set of singular points. Explicit examples show that the singular set could be, in general, as large as the regular set. In a recent paper with Ros-Oton and Serra we show that, generically, the singular set has codimension 3 inside the free boundary, solving a conjecture of Schaeffer in dimension n ≤ 4. The aim of this talk is to give an overview of these results.
• 

  A unifying partially-interpretable framework for neural network-based extreme quantile regression

  Raphaël Huser, Associate Professor, Statistics

  Nov 29, 12:00 - 13:00

  B9 L2 R2322

  Artificial Neural Network quantile regression

  In this paper, we propose a new methodological framework for performing extreme quantile regression using artificial neural networks, which are able to capture complex non-linear relationships and scale well to high-dimensional data.
• 

  Opportunities and Challenges in Free Space Optics

  Prof. Ozdal Boyraz, University of California, Irvine, USA

  Nov 28, 14:15 - 15:00

  B2 5220

  nter-Satellite Communications PNT satellite communication FSO systems

  Opportunities and Challenges in Free Space Optics: Inter-Satellite Communications, Lidars, and PNT (Positioning, Navigation, and Timing). Abstract Recent developments in communications, such as providing broadband access to rural areas and enabling high bandwidth inter-satellite communications open new opportunities in free space optical communications. In this talk, recent testbed development efforts on more than 10km free space optical communication link development with more than 80Gb/s data capacity under the NSF PAWR program in rural Iowa State. Additionally, present challenges associated
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  Data Assimilation for Flood Real Time Estimation: Challenges and Methods

  Prof. Christian Claudel, The University of Texas at Austin

  Nov 28, 13:15 - 14:00

  B2 5220

  data assimilation real time estimation

  Abstract Flash floods are one of the most common natural disasters worldwide, causing thousands of casualties every year. A combination of fixed and mobile sensors can be used to track these events. In particular, the emergence of Unmanned Aerial Vehicles (UAVs) could enable the monitoring of floods over large geographical areas. In this talk, we focus on the problems of sensing, data assimilation, and trajectory planning for a swarm of mobile agents (for example UAVs) sensing floods We first show that deep-learning can efficiently approximate the flood evolution over time, given external
• 

  Modelling and Simulation of Biological Systems - 2022-11-28

  Gabriel Wittum, Professor, Applied Mathematics and Computational Sciences

  Nov 28, 12:00 - 13:00

  B2 L5 R5209

  applied mathematics bioengineering

  Biological systems are distinguished by their enormous complexity and variability. That is why mathematical modelling and computational simulation of those systems is very difficult, in particular thinking of detailed models which are based on first principles. The difficulties start with geometric modelling which needs to extract basic structures from highly complex and variable phenotypes, on the other hand also has to take the statistic variability into account.
• 

  Bridging the Gap between the Academia and Industry by Extending High-Quality Research to Practical Business Needs

  Dr.Syed Adnan Yusuf

  Nov 28, 12:00 - 13:00

  B9 L2 R2322 H1

  Computer Vision robotics automation IoT nlp artificial intelligence

  This seminar focuses on providing the audience with the context and scope of our internship program. The program is for the young and talented graduate students with an active interest in solving real-world problems. Some of the projects that will be presented in the seminar are actively developed in Elm and include domains such as computer vision, robotics and automation, healthcare, IoT, video analytics, and NLP. The seminar will serve as a launch pad to allow students to discuss their future interests and aspirations with the speaker. It will also enable them to develop a better awareness of domains more relevant to their future research aspirations.
• 

  Towards Enhancing Security in Intelligent Surface-enabled Wireless Networks

  Prof. Galymzhan Nauryzbayez, Nazarbayev University, Kazakhstan

  Nov 28, 10:45 - 11:30

  B2 5220

  wireless networks

  Abstract The rampage of incessant cyber attacks have caused the disclosure of billions of users’ private data. In response, various data privacy laws and regulations have emerged, forcing the academia and industry to change their practice and bringing the demand for large-scale secure wireless networks to the spotlight. Such a demand, however, cannot be met by the state-of-the-art cryptographic techniques. To narrow the gap, recent years have seen rapid progress in physical layer security (PLS) solutions. In this talk, I will present our research on understanding and addressing the security
• 

  Formal Verification of Neural Network Controlled Cyber-Physical Systems

  Prof. Yasser Shoukry, University of California, Irvine, USA

  Nov 28, 10:00 - 10:45

  B2 5220

  Resilient

  Abstract Deep Neural Networks (DNNs) are increasingly being used to control physical/mechanical systems. Self-driving cars, drones, and smart cities are just examples of such systems to name a few. However, regardless of the explosion in the use of DNNs within a multitude of cyber-physical systems (CPS) domains, the safety, and reliability of these DNN-controlled CPS is still an understudied problem. Mathematically based techniques for the specification, development, and verification of software and hardware systems, also known as formal methods, hold the promise to provide appropriate
• 

  Model-based Design of Cyber-physical Systems with Learning Components

  Prof. Panagiotis Katsaros, Aristotle University of Thessaloniki

  Nov 27, 14:30 - 15:15

  B2 5220

  cyber-physical systems

  Abstract Cyber-physical system design involves heterogeneous components for sensing, control, actuation, networking and relies on models for the system’s physical dynamics, and all interactions with its environment. Since safety and security are critical, model-based engineering methods are often used to increase the confidence and guide the validation testing of the system under design. An abundant of results from modern system theory (discrete, continuous and hybrid) provides the foundations for modeling, simulation, testing and formal verification. Mature tools and languages offer advanced
• 

  Cross-Layer Security of Embedded and Cyber-Physical Systems

  Prof. Mohammad Alfaruque, University of California, Irvine, USA.

  Nov 27, 13:45 - 14:30

  B2 5220

  cyber-physical systems

  Abstract Cyber-physical systems (CPS) are engineered systems that are built from, and depend upon, the seamless integration of computation and physical components . Embedded systems comprising of hardware and software systems are the major enabling technology for these cyber-physical systems. Today, CPSs can be found in security-sensitive areas such as aerospace, automotive, energy, healthcare, manufacturing transportation, entertainment, and consumer appliances. Compared to the traditional information processing systems, due to the tight interactions between cyber and physical components
• 

  On the collapse mechanism of Wave Maps

  Prof. Manoussos Grillakis, Departments of Mathematics, University of Maryland

  Nov 27, 13:00 - 15:00

  B1 L4 R4214

  Partial Differential Equations

  The Wave Map system describes the evolution of waves constrained on a (Riemannian) manifold. For the 2 + 1 dimensional problem, when the target manifold is a sphere, the solution collapses in finite time. The Analysis is due to the pioneering work of Merle, Paphael and Rodnianski. Motivated by their work I will present a somewhat novel approach of the collapsing mechanism which is based on a view of the equations as a nonlinear gauge system. This is joint work with Dan Geba.
• 

  A journey in applied complexity: pioneering record-performing technologies in clean energy harvesting, machine vision, and smart materials - Graduate Seminar

  Andrea Fratalocchi, Professor, Electrical and Computer Engineering

  Nov 27, 12:00 - 13:00

  B9 L2 R2322 H1

  Applied complexity

  Complexity studies physical processes that are generally unpredictable or difficult to predict and depend on many degrees of freedom. In this talk, I will summarize my group's recent research, discussing present results and future challenges of Applied complexity both as a science and engineering.
• 

  Hardware security and trust for wireless integrated circuits

  Prof. Haralampos Stratigopoulos, Sorbonne Université

  Nov 27, 10:15 - 11:30

  B2 5220

  Hardware Network wireless

  Abstract We will discuss Integrated Circuit (IC) security and trust threats and corresponding countermeasures with a focus on wireless ICs. We will present solutions to protect RF transceiver ICs against piracy attempted by malicious System-on-Chip (SoC) integrators, foundries, or end-users via reverse-engineering. We will also present hardware Trojan attacks on RF transceivers aiming at creating a covert channel for stealing sensitive information, i.e., cypher keys. Brief Biography Haralampos-G. Stratigopoulos received the Diploma in electrical and computer engineering from the National
• 

  Trust, But Verify: Towards Self-Aware, Safe, Autonomous Self-Driving Systems

  Prof. Fadi Kurdahi

  Nov 27, 09:30 - 10:15

  B2 5220

  Autonomous system

  Abstract Mobile autonomous devices use sophisticated algorithms to automatically navigate diverse physical environments in a safe manner. Guaranteeing system safety while optimizing resource-usage of an autonomous device’s computer is an outstanding challenge. Runtime verification helps provide robustness by monitoring application execution at runtime. These systems operate in dynamic environments, requiring self-aware runtime management to maximize lifetime while enforcing safe execution. Safe execution and lifetime management require consideration of performance and efficiency, two
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  Embedded and Cyber Connected Systems (ECCS) Workshop

  Prof. Ahmed Eltawil, Prof. Charalambos Konstantinou, Prof. Khaled Nabil Salama

  Nov 27, 08:00 - Nov 28, 17:00

  B2 L5 R5220

  cybersecurity machine learning 5G networks hardware security

  The workshop aims to bring together experts to present their latest research efforts related to Embedded and Cyber Connected Systems architectures and platforms that can scale efficiently, as well as operate securely and resiliently to provide the necessary resources demanded by current and future network applications.

Nov 20 - Nov 26, 2022

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  Introduction to Quantum Computing

  Arnaud Carignan Dugas, Research Scientist, Quantum Computing University of Waterloo

  Nov 23, 14:00 - 16:00

  Online Event

  quantum computing

  In this presentation, we will review past, present, and future eras of quantum computing - such as Near Intermediate Scale Quantum (NISQ) and Utility Scale Quantum (USQ) eras – and cover their respective overarching aspirations and limitations. Once properly situated, we can identify the different roles that academic and industrial players are taking to drive this emerging technology and circumvent current systems' limitations.
• 

  Machine learning in hardware via trained metasurface encoders: theory, design and applications

  Maksim Makarenko, Ph.D. Student, Electrical and Computer Engineering

  Nov 23, 10:00 - 12:30

  B2 L5 R5209

  machine learning artificial intelligence photonics

  In this thesis, we introduce a novel concept of metasurface optical accelerators for machine learning with the corresponding end-to-end optimization framework that is robust to fabrication intolerance and can simultaneously optimize in tens of millions of degrees of freedom. The core of this technology is universal approximators, a single surface of optical nanoresonators mathematically equivalent to a single layer of an artificial neural network (ANN).
• 

  Sharp Approximation Rates for Deep ReLU Neural Networks on Sobolev Spaces

  Assistant Professor Jonathan Siegel, Texas A and M University

  Nov 22, 15:30 - 17:00

  B1 L3 R3119

  ReLU Neural Network Sobolev Spaces

  Sobolev spaces are centrally important objects in PDE theory. Consequently, to understand how deep neural networks can be used to numerically solve PDEs a necessary first step is to determine now efficiently they can approximate Sobolev functions. In this talk we consider this problem for deep ReLU neural networks, which are the most important class of neural networks in practical applications.
• 

  Lipschitz learning and the infinity-Laplacian

  Miguel Urbano, Professor, Applied Mathematics and Computational Sciences

  Nov 22, 12:00 - 13:00

  B9 L2 R2322

  Lipschitz additive nonlinear uncertainty infinity Laplacian

  Infinity-harmonic functions have recently found application in Semi-Supervised Learning, in the context of the so-called Lipschitz Learning. With this application in mind, we will discuss the Lipschitz extension problem, its solution via MacShane-Whitney extensions and its several drawbacks, leading to the notion of AMLE (Absolutely Minimising Lipschitz Extension).
• 

  On training algorithms for neural networks

  Jinchao Xu, Professor, Applied Mathematics and Computational Sciences

  Nov 21, 12:00 - 13:00

  B9 L2 R2322 H1

  Deep learning deep neural networks

  In this talk, I will first give a convergence analysis of gradient descent (GD) method for training neural networks by relating them with finite element method. I will then present some acceleration techniques for GD method and also give some alternative training algorithms
• 

  Principles and Algorithms for Multi-Agent Decision Making

  Shinkyu Park, Assistant Professor, Electrical and Computer Engineering

  Nov 20, 12:00 - 13:00

  B9 L2 R2322 H1

  Multi-Agent Decision Making

  A multi-agent system consists of individual agents sharing information and coordinating for collective decision making. The study of multi-agent decision making has important implications in conceiving networked engineering systems - a team of mobile robots or a fleet of drones - that can effectively coordinate to carry out assigned missions. Modeling such system as feedback interconnections of many smaller units allows us to examine its long-term behavior using analytical tools from feedback control theory, such as Lyapunov stability and bifurcation analysis. In this presentation, we discuss how such tools can be used to predict asymptotic behavior of the agents' decision making process and also to design computational models of the decision making process.

Nov 13 - Nov 19, 2022

• 

  Interval Prediction for Spatial Data Using Deep Learning Models

  Ghulam Qadir, Posdoctoral fellow, Computational Statistics group at Heidelberg Institute for Theoretical Studies, Germany

  Nov 17, 10:00 - 11:00

  B1 L4 R4102

  Statistical analysis for the purpose of prediction is preferably accompanied by uncertainty quantification, often in the form of prediction intervals. Deep learning approaches have been extensively shown to provide accurate point predictions in many applications.
• 

  Functional Differential Equations in Models of Collective Behavior - Graduate Seminar

  Jan Haskovec, Senior Research Scientist, Applied Mathematics and Computational Sciences

  Nov 15, 12:00 - 13:00

  B9 L2 R2322

  Functional Differential Equation

  The talk will give an overview of recent results for models of collective behavior governed by functional differential equations. It 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.
• 

  Recent Progress in Aeronautical Sciences: Exquisite Stability of Insect Flight and a New Theory of Lift

  Prof. Haithem Taha, Associate Professor, Department of Mechanical and Aerospace Engineering at the University of California, Irvine

  Nov 14, 12:00 - 13:00

  B2 L5 R5220

  Aeronautical Sciences Insect Flight New Theory of Lift

  In this talk, I will present a special variational principle that we revived from the history of analytical mechanics: Hertz’ principle of least curvature. Using this principle, we developed a general (dynamical) closure condition that is, unlike the Kutta condition, derived from first principles. In contrast to the classical theory, the proposed variational theory is not confined to sharp-edged airfoils, i.e., it allows, for the first time, theoretical computation of lift over arbitrarily smooth shapes, thereby generalizing the century-old lift theory of Kutta and Zhukovsky. Moreover, the new variational condition reduces to the Kutta condition in the special case of a sharp-edged airfoil, which challenges the widely accepted concept regarding the viscous nature of the Kutta condition. We also generalized this variational principle to Navier-Stokes’, thereby discovering the fundamental quantity that Nature minimizes in every incompressible flow.
• 

  Parameter-Free Online Optimization: Past, Present, and Future

  Francesco Orabona, Associate Professor of Electrical and Computer Engineering, Boston University

  Nov 14, 12:00 - 13:00

  B9 L2 R2322

  Parameter-free online optimization machine learning

  Parameter-free online optimization is a class of algorithms that does not require tuning hyperparameters, yet they achieve the theoretical optimal performance. Moreover, they often achieve state-of-the-art performance too. An example would be gradient descent algorithms completely without learning rates. In this talk, I review my past and present contributions to this field. Building upon a fundamental idea connecting optimization, gambling, and information theory, I discuss selected applications of parameter-free algorithms to machine learning and statistics. Finally, we conclude with an overview of the future directions of this field.
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  Multifunctional Electronic Devices: Towards Sustainable Electronics

  Nazek El-Atab, Assistant Professor, Electrical and Computer Engineering

  Nov 13, 12:00 - 13:00

  B9 L2 R2322 H1

  Multifunctional Electronic Devices

  In this talk, I will discuss multi-functional smart electronic devices which can perform the roles of multiple conventionally discrete components at once.

Nov 6 - Nov 12, 2022

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  Introduction to Computer Simulation - A short course II - Part 3

  Prof. Michal Mankowski, Assistant Professor of Operations Research, Erasmus University Rotterdam, Netherlands

  Nov 10, 10:00 - 11:30

  B1 L3 R3119

  The aim of this course is to familiarize the students with the usage of Computer Simulation tools for complex problems. The course will introduce the basic concepts of computation through modeling and simulation that are increasingly being used in industry and academia. The basic concepts of Discrete Event Simulation will be introduced along with the reliable methods of random variate generation and variance reduction. Later in the course, the concept of simulation-based optimization and output analysis will be discussed. The example of simulation (and optimization) applied to design an optimal organ allocation policy in the US will be discussed.
• 

  Introduction to Computer Simulation - A short course II - Part 2

  Prof. Michal Mankowski, Assistant Professor of Operations Research, Erasmus University Rotterdam, Netherlands

  Nov 9, 10:00 - 11:30

  B1 L3 R3119

  The aim of this course is to familiarize the students with the usage of Computer Simulation tools for complex problems. The course will introduce the basic concepts of computation through modeling and simulation that are increasingly being used in industry and academia. The basic concepts of Discrete Event Simulation will be introduced along with the reliable methods of random variate generation and variance reduction. Later in the course, the concept of simulation-based optimization and output analysis will be discussed. The example of simulation (and optimization) applied to design an optimal organ allocation policy in the US will be discussed.
• 

  Parallel multilevel solvers for virtual element discretizations of saddle point problems

  Prof. Simone Scacchi, Associate Professor of Numerical Analysis at the Department of Mathematics of the University of Milan

  Nov 8, 15:30 - 17:00

  B1 L3 R3119

  Virtual Element Method

  In this seminar, we will present our work on Virtual Element Method (VEM) approximations. The Virtual Element Method is a recent numerical technique for solving partial differential equations on computational grids constituted by polygonal or polyhedral elements of very general shape. This work aims to develop effective linear solvers for general order VEM approximations of three-dimensional scalar elliptic equations in mixed form and Stokes equations. To this end, we consider block algebraic multigrid preconditioners and balancing domain decomposition by constraints (BDDC) preconditioners. The latter allows us to use conjugate gradient iterations, albeit the algebraic linear systems arising from the discretization of the differential problems are indefinite, ill-conditioned, and of saddle point nature.
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  The role of skewed distributions in Bayesian inference: conjugacy, scalable approximations and asymptotics

  Daniele Durante, Assistant Professor of Statistics at the Department of Decision Sciences, Bocconi University, Italy

  Nov 8, 15:00 - 16:00

  B1 L4 R4102

  bayesian inference

  In this talk, I will review, unify and extend recent advances in Bayesian inference and computation for such a class of models, proving that unified skew-normal (SUN) distributions (which include Gaussians as a special case) are conjugate to the general form of the likelihood induced by these formulations. This result opens new avenues for improved posterior inference, under a broad class of widely-implemented models, via novel closed-form expressions, tractable Monte Carlo methods based on independent and identically distributed samples from the exact SUN posterior, and more accurate and scalable approximations from variational Bayes and expectation-propagation. These results will be further extended, in asymptotic regimes, to the whole class of Bayesian generalized linear models via novel limiting approximations relying on skew-symmetric distributions.
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  Modeling water waves: from tsunamis to the kitchen sink

  David Ketcheson, Professor, Applied Mathematics and Computational Sciences

  Nov 8, 12:00 - 13:00

  B9 L2 R2322

  water waves Tsunamis Statistical Modelling

  Surface water waves are a physically important phenomenon with which we all have some experience. They are also surprisingly complex and interesting from a mathematical perspective. I will discuss two recent projects in water wave modeling. The first deals with ocean waves, such as tsunamis, passing over the continental slope. It has long been known that the amplification of such waves is greater than what the traditional transmission coefficient would predict.
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  Introduction to Computer Simulation - A short course II - Part 1

  Prof. Michal Mankowski, Assistant Professor of Operations Research, Erasmus University Rotterdam, Netherlands

  Nov 8, 10:00 - 11:30

  B1 L3 R3119

  The aim of this course is to familiarize the students with the usage of Computer Simulation tools for complex problems. The course will introduce the basic concepts of computation through modeling and simulation that are increasingly being used in industry and academia. The basic concepts of Discrete Event Simulation will be introduced along with the reliable methods of random variate generation and variance reduction. Later in the course, the concept of simulation-based optimization and output analysis will be discussed. The example of simulation (and optimization) applied to design an optimal organ allocation policy in the US will be discussed.
• 

  Numerical approximations of Mean-Field-Games

  Serikbolsyn Duisembay, Ph.D. Student, Applied Mathematics and Computational Sciences

  Nov 7, 14:00 - 16:00

  B1 L2 R2202

  mean-field games

  In this thesis, we present three projects. First, we investigate the numerical approximation of Hamilton-Jacobi equations with the Caputo time-fractional derivative. We introduce an explicit in time discretization of the Caputo derivative and a finite-difference scheme for the approximation of the Hamiltonian. We show that the approximation scheme is stable under an appropriate condition on the discretization parameters and converges to the unique viscosity solution of the Hamilton-Jacobi equation.
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  Thoughts on how to Effectively Explore Visual Representations in Mixed Reality

  Tobias Isenberg, Senior Research Scientist, Inria

  Nov 7, 12:00 - 13:00

  B9 L2 R2322 H1

  virtual reality data visualisation Scientific Visualization

  In this talk I will report on various research projects that I carried out with my students to better understand the interaction landscape and will report on lessons we learned. I will focus mostly on AR-based setups with application examples from physical flow visualization, molecular visualization, visualization of particle collisions, biomolecular dynamics in cells, and oceanography. I will show interaction techniques that rely on purely gestural interaction, phones or tablets as input and control devices, and hybrid setups that combine traditional workstations with AR views. I will discuss navigation, data selection, and visualization system control as different interaction tasks. With this overview I aim to provide an understanding of typical challenges in immersive visualization environments and how to address some of these challenges.
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  Multivariate Functional Data Analysis and Visualization

  Zhuo Qu, Ph.D. Student, Statistics

  Nov 7, 11:00 - 13:00

  B3 L5 R5220

  functional data analysis multivariate functional data statistics

  As a branch of statistics, functional data analysis studies observations regarded as curves, surfaces, or other objects evolving over a continuum. Current methods in functional data analysis usually require data to be smoothed and analyzed marginally, which may hide some outlier information or take extra time on pretreating the data. After exploring model-based fitting for regularly observed multivariate functional data, we explore new visualization tools, clustering, and multivariate functional depths for irregularly observed (sparse) multivariate functional data.
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  Integrating Terrestrial and Non-terrestrial Networks: 3D Opportunities and Challenges

  Giovanni Geraci, Assistant Professor, Pompeu Fabra University, Barcelona

  Nov 6, 16:00 - 17:00

  B1 L3 R3119

  Integrating Terrestrial and Non-terrestrial Networks

  In a quest for anything, anytime, anywhere connectivity, next-generation wireless networks are envisioned to break the boundaries of the current ground-focused paradigm and fully embrace aerial and spaceborne communications.
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  Towards sustainable and resilient multi-energy infrastructure systems: water-energy nexus

  Mohannad Alhazmi, Electrical Engineering, King Saud University

  Nov 6, 12:00 - 13:00

  B9 L2 R2322 H1

  With the widespread deployment of advanced heterogeneous technologies and the sharply-growing complexity in our modern society, there is an increasing demand for risk-aware management and joint operation of interconnected infrastructures and lifeline networks.

Oct 30 - Nov 5, 2022

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  Flexible Electronics Beyond Mechanical Flexibility: Multifunctional Flexible Semiconductor Materials and Devices for Photonic, Electronic, Energy, and Sensing Applications

  Prof. Jae-Hyun Ryou, Mechanical Engineering, University of Houston

  Nov 3, 12:00 - 13:00

  B9 L3 R3223

  flexible photonic energy sensing devices

  Flexible electronics is an emerging and widely explored area. Most research groups in the area focus on fabrication processes to provide mechanical flexibility and their use in bendable and stretchable applications. Also, most semiconductors employed in flexible electronics are non-single-crystalline thin films which compromise the performance of the flexible devices.
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  Wide Bandgap Semiconductor Device Design via Machine Learning

  Rongyu Lin, Ph.D., Electrical and Computer Engineering

  Nov 2, 15:30 - 17:30

  B2 L5 R5220

  machine learning light gradient boosting machine extreme gradient boosting

  This dissertation presents novel approaches to the design of electrical and optical wide bandgap semiconductor devices, which opens new avenues for future research. It is possible that it might be used in a broad variety of sectors, including illumination, sensing, disinfection, and power devices by using TCAD and machine learning to deliver quick forecasts of the III-nitride semiconductor device.
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  Autonomy for recreational vessels: Safe intelligence at your finger tips Pablo Afman, ATL Robotics

  Juan-Pablo Afman

  Nov 2, 12:00 - 13:00

  B2 L5 R5220

  robotics intelligent systems

  Abstract Thanks to ever improving sensing, computing, and actuating technologies, autonomy is fast becoming a practical tool for many marine applications. Pushed by the general interest in autonomy and attracted by its underlying capabilities, this talk will present robotic technologies that enable safety and convenience for recreational boaters and marine professionals alike. Beginning with a Grady-White offshore fishing boat powered by two 425 HP engines, we present demonstrated functionalities such as collision avoidance, auto-docking, dynamic positioning, track and follow, and "take me
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  Numerical approximation of McKean-Vlasov SDEs via Stochastic Gradient Descent

  Prof. Gonçalo dos Reis, School of Mathematics, University of Edinburgh

  Nov 1, 15:30 - 17:00

  B1 L3 R3119

  stochastic gradient descent McKean-Vlasov SDEs

  We propose a novel approach of numerically approximate McKean-Vlasov SDEs that avoids the usual interacting particle approximation and Propagation of Chaos results altogether.
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  Multilevel Bayesian Neural Networks

  Nov 1, 12:00 - 13:00

  B9 L2 R2322

  Bayesian computational technique Applied Machine Learning

  Abstract In this talk we consider the application of multilevel Monte Carlo for Bayesian computation tasks in machine learning. There has recently been a synergy of statisticsand machine learning, promoting the application and development of new methodologies.Based on this we promote the use of multilevel Monte Carlo, which is a technique used toreduce the cost to attain a particular order of MSE with trace-class neural network priors. We provide some theoretical insights, and demonstrate the performance of our methodologyon different model problems such as classification and reinforcement
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  Microstructural Analysis and Engineering of III-Nitride-Based Heterostructures for Optoelectronic Devices

  Martín Velázquez Rizo, Ph.D. Student, Electrical and Computer Engineering

  Nov 1, 10:00 - 11:30

  B1 L3 R3119

  photocatalytic energy conversion

  This dissertation presents a variety of methodological approaches to characterize, from a microstructural point of view, different properties of novel III-nitride-based heterostructures and devices. The results of the various characterizations contributed to developing novel LEDs and photocatalysts. The analyses and results presented in this dissertation strongly relied on the analytical capabilities offered by transmission electron microscopy, which proved to be a convenient and versatile tool for the characterization of many aspects related to the fabrication of III-nitride-based optoelectronic devices.
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  Everything PhD students always wanted to know about research* (*but were afraid to ask)

  Oct 31, 12:00 - 13:00

  B9 L2 R2322 H1

  scientific research PhD student

  From my experience, I will try to answer doubts and dilemmas PhD students are often faced with, in their path towards a degree. Namely, I'll discuss how advisors, colleagues, peers, reviewers and so forth, fit in the universe of a PhD student, and I will end sharing my own definition of 'excellence', as an objective to pursue.
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  The KAUST 2022 Workshop on Statistics

  Oct 31, 09:00 - Nov 3, 17:00

  B2 B3 A0215

  statistics data science big data modeling complex data

  The workshop will cover current trends in statistics and the statistical modeling of big and complex data. Over four days, it will have talks by leading experts as well as two poster sessions where students and postdocs will present their work. All talks will be given Auditorium 0215, between Buildings 2 and 3 at KAUST. Speakers Aritz Adin (Public University of Navarre) Ahmadou Alioum (Bordeaux School of Public Health) Denis Allard (INRAE) Vera Baladandayupathani (University of Michigan) Emily Hector (North Carolina State University) Eduardo Garcia Portugues (Carlos III University of Madrid)
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  Non-Terrestrial Networks: Opportunities and Challenges

  Abdulah Aljohani, Research and Innovation Consultant, Communications and Information Technology Commission (CITC)

  Oct 30, 16:00 - 16:30

  KAUST

  Non-Terrestrial Networks

  Next-generation wireless networks are envisioned to break the boundaries of the current terrestrial-based networks and fully adopt the Non-Terrestrial Networks (NTNs).
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  Toward scalable and high performance on-chip light source for integrated silicon photonics

  Chen Shang, Postoctoral Researcher Fellow, University of California Santa Barbara

  Oct 30, 12:00 - 13:00

  B9 L2 R2322

  Integrated Photonics technology

  Monolithic integration of quantum dot (QD) gain materials onto Si photonic platforms via direct epitaxial growth is considered as the ultimate solution for on-chip light sources.