Events

Jun 4 - Jun 11, 2023

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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.

May 28 - Jun 11, 2023

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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 4, 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.

May 21 - Jun 4, 2023

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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 28, 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.
• 

  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.
• 

  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.

May 14 - May 28, 2023

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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.
• 

  Stochastic Numerics and Statistical Learning: Theory and Applications Workshop 2023

  Stochastic Numerics PI Professor Raul Tempone (Chair) and Computational Probability PI Professor Ajay Jasra (Co-Chair)

  May 21, 08:00 - Jun 1, 17:00

  B9

  stochastic algorithms statistical learning machine learning

  Dear Kaustians, We are excited to announce the upcoming Stochastic Numerics and Statistical Learning: Theory and Applications Workshop 2023, taking place at KAUST, Building 9, from May 21 to June 1, 2023. Following the highly successful 2022 edition, this year's workshop promises to be another engaging and insightful event for researchers, faculty members, and students interested in stochastic algorithms, statistical learning, optimization, and approximation. The 2023 workshop aims to build on the achievements of last year's event, which featured 28 talks, two mini-courses, and two poster sessions, attracting over 150 participants from various universities and research institutes. In 2022, attendees had the opportunity to learn from through insightful talks, interactive mini-courses, and vibrant poster sessions. This year, the workshop will once again showcase contributions that offer mathematical foundations for algorithmic analysis or highlight relevant applications. Confirmed speakers include renowned experts from institutions such as Ecole Polytechnique, EPFL, Université Pierre et Marie Curie - Paris VI, and Imperial College London, among others.

May 14 - May 21, 2023

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  Viability assays of CMOS-compatible materials, device architectures, and emerging device platforms for nano-photonics

  Nasir Alfaraj, Ph.D., Electrical and Computer Engineering

  May 18, 15:00 - 16:00

  B5 L5 R5209

  nanophotonics

  My research at the University of Toronto involves designing and fabricating CMOS-compatible nano-plasmonic waveguide structures and devices for on-chip communication. By taking advantage of the absence of diffraction limits, strong modal confinement is made possible using plasmonic device architectures, laying the foundations for improved optical processes and photonic circuit integration. All-oxide structures have emerged as promising plasmonic materials that can serve as relatively low carrier density Drude metals and dielectric spacers by their electro-optic tunability and versatility. I demonstrated the facile integration of all-oxide heterointerfaces into metal–insulator–semiconductor (MIS) electro-optic structures. From there, I fabricated MIS devices that incorporated semiconductive oxide layers with oxide dielectric spacers on silicon-on-insulator (SOI) platforms, constituting graded-index coupled hybrid plasmonic waveguide (CHPW) optical modulators.

May 7 - May 21, 2023

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  KAUST-Fudan Joint Workshop on Intelligent Communication, Device and System Technologies

  Boon Ooi, Professor, Electrical and Computer Engineering

  May 14, 09:00 - 16:00

  B18 L4 Fellowship Hall

  Intelligent Communication photonics

  This joint workshop between KAUST and Fudan University covers a variety of topics related to optical communication and devices for visible light communication. Theoretical modeling, device engine/materials investigation and sub-system development will be discussed. These research are essential to eventually realize the goals of connecting all the unconnected, SMART systems and optical internet-of-things. The workshop will be running in hybrid mode, and presenters will be from both KAUST and Fudan University.

May 7 - May 14, 2023

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  Monotonicities and master equations in mean field games - Part 3

  Alpar Meszaros, Assistant Professor, Department of Mathematical Sciences, Durham University (UK)

  May 11, 10:30 - 12:00

  B1 L4 R4102

  The theory of mean field games (MFG for short) aims to study limiting behavior of Nash equilibria of (stochastic) differential games when the number of agents tends to infinity. While in general existence of MFG Nash equilibria can be established under fairly general assumptions, uniqueness is the exception rather than the rule.
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  A brief introduction to the obstacle problem regularity theory - Part 3

  Eduardo Teixeira, Full Professor, Department of Mathematics, University of Central Florida (USA)

  May 11, 08:30 - 10:00

  B1 L4 R4102

  How does an elastic membrane lie on a given obstacle? This naive looking question hides a beautiful mathematical theory integrating powerful tools with far-reaching applications. In this mini-course we will discuss the obstacle problem as a free boundary model. All necessary tools will be carefully constructed from scratch. We will mainly focus on: optimal regularity of solutions, non-degeneracy estimates, weak geometric-measure properties of the free boundary, classification of global profiles, and differentiability of the free boundary.
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  Towards the Inference, Understanding, and Reasoning on Edge Devices

  Guoqing Ma, Ph.D. Student, Computer Science

  May 10, 14:00 - 16:00

  B1 L3 R3119

  Edge devices refer to compact hardware that performs data processing and analysis close to the data source, eliminating the need for data transmission to centralized systems for analysis. These devices are typically integrated into other equipment, such as sensors or smart appliances, and can collect and process data in real time.
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  On some nonlinear elliptic/parabolic systems arising in mechanics and turbulence

  Prof. Luigi Carlo Berselli

  May 9, 16:00 - 17:00

  B2 L5 R5220

  nonlinear linear elliptic parabolic Systems Mechanics Turbulence

  I consider the vector counterpart of the classical p-Laplace and p-heat equations which are some of the building blocks for the mathematical description of non-linear plasticity, non-Newtonian fluids, and turbulent eddy viscosity models. I will discuss results of “natural regularity” and their role in the study of the well-posedness of the nonlinear pdes as well as in the theory of convergence for space-time discretisation methods. In particular, I will present the “A-approximation" method which generalises results by Necas and which reduces the problem to a family of linear ones. Coffee Time: 15:30 - 16:00
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  New Graphical Displays for Classification

  Peter Rousseeuw, Professor Emeritus, Statistics and Data Science, KU Leuven, Belgium

  May 9, 15:00 - 16:00

  B9 L2 H2

  Classification is a major tool of statistics and machine learning. Several classifiers have interesting visualizations of their inner workings. Here we pursue a different goal, which is to visualize the cases being classified, either in training data or in test data. An important aspect is whether a case has been classified to its given class (label) or whether the classifier wants to assign it to a different class. This is reflected in the probability of the alternative class (PAC). A high PAC indicates label bias, i.e. the possibility that the case was mislabeled. The PAC is used to construct a silhouette plot which is similar in spirit to the silhouette plot for cluster analysis. The average silhouette width can be used to compare different classifications of the same dataset. We will also draw quasi residual plots of the PAC versus a data feature, which may lead to more insight in the data. One of these data features is how far each case lies from its given class, yielding so-called class maps. The proposed displays are constructed for discriminant analysis, k-nearest neighbors, support vector machines, CART, random forests, and neural networks. The graphical displays are illustrated and interpreted on data sets containing images, mixed features, and texts.
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  Detecting Cellwise Outliers in Your Data

  Peter Rousseeuw, Professor Emeritus, Statistics and Data Science, KU Leuven, Belgium

  May 9, 12:00 - 13:00

  B9 L2 H2

  A multivariate dataset consists of n cases in d dimensions, and is often stored in an n by d data matrix. It is well-known that real data may contain outliers. Depending on the situation, outliers may be (a) undesirable errors which can adversely affect the data analysis, or (b) valuable nuggets of unexpected information. In statistics and data analysis the word outlier usually refers to a row of the data matrix, and the methods to detect such outliers only work when at least half the rows are clean. But often many rows have a few contaminated cell values, which may not be visible by looking at each variable (column) separately. We describe a method to detect deviating data cells in a multivariate sample which takes the correlations between the variables into account. It has no restriction on the number of clean rows, and can deal with high dimensions. Other advantages are that it provides predicted values of the outlying cells, while imputing missing values at the same time. We illustrate the method on several real data sets, where it uncovers more structure than found by purely columnwise methods or purely rowwise methods. The proposed method can help to diagnose why a certain row is outlying, e.g. in process control. It also serves as an initial step for estimating multivariate location and scatter matrices, and for cellwise robust principal component analysis.
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  Monotonicities and master equations in mean field games - Part 2

  Alpar Meszaros, Assistant Professor, Department of Mathematical Sciences, Durham University (UK)

  May 9, 10:30 - 12:00

  B1 L4 R4102

  The theory of mean field games (MFG for short) aims to study limiting behavior of Nash equilibria of (stochastic) differential games when the number of agents tends to infinity. While in general existence of MFG Nash equilibria can be established under fairly general assumptions, uniqueness is the exception rather than the rule.
• 

  A brief introduction to the obstacle problem regularity theory - Part 2

  Eduardo Teixeira, Full Professor, Department of Mathematics, University of Central Florida (USA)

  May 9, 08:30 - 10:00

  B1 L4 R4102

  How does an elastic membrane lie on a given obstacle? This naive looking question hides a beautiful mathematical theory integrating powerful tools with far-reaching applications. In this mini-course we will discuss the obstacle problem as a free boundary model. All necessary tools will be carefully constructed from scratch. We will mainly focus on: optimal regularity of solutions, non-degeneracy estimates, weak geometric-measure properties of the free boundary, classification of global profiles, and differentiability of the free boundary.
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  Process Monitoring Techniques and Applications

  Fouzi Harrou , Research Scientist, Statistics

  May 8, 12:00 - 13:00

  B9 L3 R3128

  Abstract Effective process monitoring is crucial to ensure engineering and environmental plants' reliable and safe operation. If anomalies in engineering or environmental plants are not detected promptly, they can affect plants' productivity, profitability, and safety. Both model-based and data-based process monitoring techniques have proven themselves in practice over the past four decades. This talk will present a model-based approach for anomaly detection in a linear model with a bounded nuisance parameter and a data-based process monitoring example using machine learning. Three real-world
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  Extraction of Complex DNN Models: Real Threat or Boogeyman? - Graduate Seminar

  Prof.N.Asokan, Computer Science, University of Waterloo

  May 8, 12:00 - 13:00

  B9 L2 H2 H2

  complex models Deep learning model extraction

  The success of deep learning in many application domains has been nothing short of dramatic. The success has brought the spotlight onto security and privacy concerns with deep learning. One of them is the threat of "model extraction": when a machine learning model is made available to customers via an inference interface, a malicious customer can use repeated queries to this interface and use the information gained to construct a surrogate model. In this talk, I will describe our work in exploring whether model extraction constitutes a realistic threat. I will also discuss possible countermeasures, focussing on deterrence mechanisms that allow for the verification of ownership of ML models.
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  Monotonicities and master equations in mean field games - Part 1

  Alpar Meszaros, Assistant Professor, Department of Mathematical Sciences, Durham University, UK

  May 8, 10:30 - 12:00

  B1 L4 R4102

  The theory of mean field games (MFG for short) aims to study limiting behavior of Nash equilibria of (stochastic) differential games when the number of agents tends to infinity. While in general existence of MFG Nash equilibria can be established under fairly general assumptions, uniqueness is the exception rather than the rule. For finite horizon mean field games uniqueness typically holds if the time horizon is small (or the data satisfies other smallness conditions).
• 

  A brief introduction to the obstacle problem regularity theory - Part 1

  Eduardo Teixeira, Full Professor, Department of Mathematics, University of Central Florida (USA)

  May 8, 08:30 - 10:00

  B1 L4 R4102

  How does an elastic membrane lie on a given obstacle? This naive looking question hides a beautiful mathematical theory integrating powerful tools with far-reaching applications. In this mini-course we will discuss the obstacle problem as a free boundary model. All necessary tools will be carefully constructed from scratch. We will mainly focus on: optimal regularity of solutions, non-degeneracy estimates, weak geometric-measure properties of the free boundary, classification of global profiles, and differentiability of the free boundary.
• 

  A brief introduction to the obstacle problem regularity theory

  Prof. Eduardo Teixeira, Department of Mathematics, University of Central Florida (USA)

  May 8, 08:30 - May 11, 10:00

  B1 L4 R4102

  free boundary problems

  Abstract How does an elastic membrane lie on a given obstacle? This naive looking question hides a beautiful mathematical theory integrating powerful tools with far-reaching applications. In this mini-course we will discuss the obstacle problem as a free boundary model. All necessary tools will be carefully constructed from scratch. We will mainly focus on: optimal regularity of solutions, non-degeneracy estimates, weak geometric-measure properties of the free boundary, classification of global profiles, and differentiability of the free boundary. Brief Biography Eduardo Teixeira is a Full

Apr 30 - May 14, 2023

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  β-Ga2O3: A robust solution for extreme environment electronics

  Vishal Khandelwal, Ph.D. Student, Electrical and Computer Engineering

  May 7, 12:00 - 13:00

  B9 L2 H2

  In this talk, I will be talking about β-Ga2O3 based monolithically integrated logic circuits and flash memory devices toward extreme environment operations. Moreover, the important challenges in material and device fabrication for realizing this technology will be discussed. Finally, I will introduce the promises of β-Ga2O3 at low-temperature (~ 4 K) operation for space electronics applications.

Apr 30 - May 7, 2023

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  Bringing the Cloud to industrial systems: trends, opportunities and challenges

  Marcello Cinque, Associate Professor, Computer Engineering, the University of Naples Federico Il, Italy.

  May 4, 15:30 - 16:30

  B4 L5 R5209

  cloud technologies industrial systems

  In recent years we are witnessing the advent of service computing and cloud technologies in industrial applications, with intriguing innovations and novel compelling challenges. For instance, in the automotive, there are initiatives for consolidating Electronic Control Units (ECUs) as virtual machines on the same board. Or in the Industry 4.0 (I4.0), researchers and practitioners are dealing with the challenge of making the factory floor programmable by softwarizing hardware elements with edge-cloud native components. The talk will delve into this novel trend, discussing enabling virtualization technologies for industrial systems, including hypervisors, real-time container-based solutions, and software orchestration approaches.
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  Query Localization in Long-form Videos

  Mengmeng Xu (Frost), Ph.D. Student, Electrical and Computer Engineering

  May 4, 07:30 - 09:00

  KAUST

  The growth of digital cameras and data communication has led to an exponential increase in video production and dissemination. As a result, automatic video analysis and understanding has become a crucial research topic in the computer vision community. However, the localization problem, which involves identifying a specific event in a large volume of data, particularly in long-form videos, remains a significant challenge.
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  GNSS Localization and Attitude Determination via Optimization Techniques on Riemannian Manifolds

  Xing Liu, Postdoctoral Research Fellow, Electrical and Computer Engineering

  May 3, 16:00 - 18:00

  B1 L3 R3119

  GNSS localization attitude determination

  This thesis focuses on Global Navigation Satellite Systems (GNSS)-based localization and attitude determination, essential for navigation and control systems in various platforms. Carrier-phase observations from GNSS signals are more accurate than pseudo-range, but resolving integer ambiguities in carrier-phase data is challenging. The thesis proposes three attitude determination methods based on an optimized GNSS attitude model with nonlinear constraints. Additionally, a joint solution for real-time kinematic positioning and attitude determination is proposed, leveraging the correlation between GNSS data in these two problems. Riemannian optimization is applied to improve the accuracy and ambiguity resolution in both localization and attitude determination.
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  Prospects for B containing III-N alloys for UVB and UVC LEDs

  Prof. Peter Parbrook, Electrical & Electronic Engineering, University College Cork

  May 3, 14:00 - 15:00

  B3 L5 R5220

  Abstract The inclusion of boron in III-Nitride semiconductors is an exciting prospect. It allow a break in the rather fixed relationship between strain, bandgap and electrical polarisation that inherently controls what can be achieved in using light emitting diodes with AlGaN. However, the addition of boron is quite challenging, leading to defects and roughening of the material, as well as difficulties in understanding of the incorporation of B in the material. In this presentation we will examine the potetnail for boron containing alloys in quantum wells, with a view to their inclusion in III
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  Modeling and Simulation of Semiconductor Equations: Insights and Applications

  Prof. Nella Rotundo, Department of Mathematics University of Florence, Italy

  May 3, 11:00 - 12:00

  B1 L4 R4102

  Abstract In this talk, we will discuss the modeling and simulation of semiconductor equations using three different formalisms. We will also describe the finite volume method and the Hybridizable Discontinuous Galerkin method as applied to semiconductor equations. These methods have proven to be highly effective in modeling semiconductor behavior, and have been used in a variety of applications such as integrated circuits, solar cells, and transistors. We will provide insights into the fundamental concepts behind these models and demonstrate their effectiveness through numerical simulations
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  Towards Data-efficient Graph Learning

  Qiannan Zhang, Ph.D. Student, Computer Science

  May 2, 18:00 - 20:00

  KAUST

  This dissertation focuses on the challenge of learning with small amounts of annotated data in graph machine learning. The scarcity of annotated data can severely degrade the performance of graph learning models, and the ability to learn with small amounts of data, known as data-efficient graph learning, is essential for achieving strong generalization in low-data regimes. The dissertation proposes three methods to address the challenges of graph learning in low-data scenarios, including a graph meta-learning framework, a solution for few-shot graph classification, and a cross-domain knowledge transfer model. Experimental results demonstrate the effectiveness of the proposed methods in improving model generalization for data-efficient graph learning.
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  Managing subsurface uncertainties in reservoir simulations: Applications to compositional flow and geothermal extraction

  Prof. Hussein Hoteit, Energy Resources and Petroleum Engineering, KAUST

  May 2, 16:00 - 17:00

  B2 L5 R5220

  Managing Subsurface reservoir Simulations compositional flow geothermal extraction

  Reservoir simulation is an essential tool in reservoir engineering, enabling effective management and optimization of reservoirs. Despite minimal changes in the fundamental sciences since the development of reservoir simulators, advancements in computational power and linear solvers have significantly improved the technology. However, the reliability and ability to improve decision quality of reservoir models remain debatable.
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  Leveraging Power Systems’ Modularity to support Resiliency Against Synthetic Cyber Disasters

  Dr. Salam Baniahmed, Technical Manager, Veloce Energy

  May 2, 15:00 - 16:00

  B1 L4 R4214

  IEEE power systems resilience cybersecurity

  The fact of power systems’ criticality to national security mandates continuous attention to the worst-case disruption scenarios. In a cyber world, grid modernization efforts have been hindered by cyber-related concerns. This webinar sheds some light on resiliency achievement by utilizing the modular cyber-physical nature of power systems, from an electron in a battery to a digital bit in the internet abyss.
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  Fabrication of metasurfaces operating in the visible via nanotechnology and artificial intelligence

  Fedor Getman, Ph.D. Student, Electrical and Computer Engineering

  May 2, 14:00 - 16:00

  B3 L2

  This thesis investigates the potential of flat optics as a solution to the problem of bulky and expensive optical components in producing lightweight and wearable optoelectronic devices. The research addresses scalability challenges in structure fabrication, design of broadband functionalities, and increasing operational and transmission efficiency in the visible range. It focuses on the experimental part of the challenge. The study evaluates various design approaches, including inverse designs using optimization techniques as well as the use of machine learning algorithms. The thesis aims to explore a path toward high efficiency, wide bandwidth, functional response, and scalable fabrication in flat optics using semiconductor nanostructures. The results demonstrate the potential of using semiconductor nanostructures to engineer efficient, scalable, and broadband optical components for light processing via flat surfaces.
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  Student Poster Competition: The KAUST Research Conference on Computational Advances in Structural Biology

  Xin Gao, Program Chair, Computer Science

  May 2, 12:15 - 14:00

  B9 Hallway

  structural biology

  The Computational Bioscience Research Center (CBRC) will be holding a student poster competition as part of its yearly conference. This poster competition is open to KAUST students whose research is relevant to the conference theme ‘Computational Advances in Structural Biology’. To register, please click on the register button below. Once registered, you will receive instructions and submission codes for uploading your poster electronically. Register The deadline for submissions is April 1, 2023. The poster competition will be judged by an external panel of experts, and prizes will be awarded
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  The 2nd SAAI Factory Hackathon Kickoff Symposium at KAUST

  The 2nd SAAI Factory Hackathon Kickoff Symposium 2023

  May 2, 09:00 - May 4, 17:00

  B20 Auditorium

  Hackathon artificial intelligence

  We are pleased to invite you to the second SAAI (Super Artistic AI) Factory Hackathon 2023, a program chaired by Christoph Faulhaber (Conceptual Artist, Film Maker) and Mohamed Elhoseiny (Computer Science Faculty at KAUST). About SAAI Factory Hackathon Established in 2021, SAAI Factory has been an initiative and one of the first contemporary art-based hackathons on Artificial Intelligence. The hackathon provides a framework for hackers, artists, coders, designers, tech evangelists, creatives, and developers from all disciplines to come together and explore the possibilities opened up by
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  PPP-RTK: a review and recent developments

  Peter Teunissen, Professor, Geodesy and Satellite Navigation, Delft University of Technology (DUT), Netherlands Honorary Professor at Melbourne and Curtin Universities, Australia, and Beihang and Tongji Universities, China

  May 1, 13:00 - 14:00

  B1 L3 R3119

  PPP-RTK GPS Galileo QZSS IRNSS

  GNSS PPP-RTK is an integer ambiguity resolution enabled precise point positioning concept originally developed for use with the ultra-precise CDMA-based Global Navigation Satellite System carrier-phase signals. In this presentation, we first present the classical principles of PPP-RTK as they apply to the CDMA-based global and regional satellite navigation systems GPS, BeiDou, Galileo, QZSS and IRNSS.
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  Towards a Universal Knowledge Graph Chatbot: ChatGPT versus Traditional Question Answering for Knowledge Graphs

  Prof.Essam Mansour, Computer Science and Software Engineering, Concordia University

  May 1, 12:00 - 13:00

  B9 L2 H2 H2

  ChatGPT conversational AI QASs knowledge graphs

  Conversational AI and Question-Answering systems (QASs) for knowledge graphs (KGs) are both emerging research areas: they empower users with natural language interfaces for extracting information efficiently and effectively. While Conversational AI simulates human-like conversations, its effectiveness is limited by the available training data. However, QASs retrieve the most up-to-date information from KGs by translating natural language queries into formal queries that the database engine can process. In this talk, we examine the characteristics of existing approaches for combining Conversational AI and QASs to create novel KG chatbots. We also introduce KGQAn, a universal QA system that can be applied to any KG without the need for customization.
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  Sub-dimensional Mardia measures of multivariate skewness and kurtosis

  Joydeep Chowdhury, Postdoctoral Research Fellow, Statistics

  May 1, 12:00 - 13:00

  B9 L3 R3128

  The Mardia measures of multivariate skewness and kurtosis summarize the respective characteristics of a multivariate distribution with two numbers. However, these measures do not reflect the sub-dimensional features of the distribution. Consequently, testing procedures based on these measures may fail to detect skewness or kurtosis present in a sub-dimension of the multivariate distribution. We introduce sub-dimensional Mardia measures of multivariate skewness and kurtosis, and investigate the information they convey about all sub-dimensional distributions of some symmetric and skewed families of multivariate distributions.
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  Robust and Energy-Efficient Embedded Computing

  Semeen Rehman, Assistant Professor, Electrical Engineering and Information Technology, TU Wien

  May 1, 12:00 - 13:00

  B9 L1 R3131

  Embedded systems are currently an indispensable part of our lives because of their pervasive deployment in a wide range of critical applications (e.g., automotive, encryption, and healthcare, etc.) as well as non-critical applications (e.g., image/video processing, etc.). These embedded system form the fundamental components of today’s Cyber Physical Systems (CPS) and Internet-of-Things (IoT), which are subjected to stringent constraints in terms of reliability, security, and power-/energy especially in case of battery-driven scenarios. Due to the shrinking transistor dimensions, embedded computing hardware are increasingly susceptible to a wide range of reliability threats e.g., transient faults (such as soft errors due to high-energy particle strikes) and permanent faults due to design-time process variations and run-time aging effects. These threats may lead to functional and timing errors that may jeopardize the correct application’s executions. Furthermore, security has also become a crucial aspect in today’s systems because of several security threats, e.g., confidentiality threat to steal the IP or private information via side channel attacks. These reliability and security threats may lead to a catastrophic impact on the robustness of embedded systems. Another key design constraint of a battery-driven embedded platform is power-/energy-wise efficiency, e.g., a device under a low power/battery mode during the critical application execution may miss a critical event that may lead to a catastrophic outcome. In order to address the above-mentioned challenges, it is crucial to investigate different techniques at the hardware and software layers. In this talk, I will first highlight the key robustness and energy efficiency challenges in embedded computing systems, and will present techniques to address these challenges.
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  KAUST Research Conference 2023 on Computational Advances in Structural Biology

  Xin Gao, Program Chair, Computer Science

  May 1, 08:00 - May 3, 17:00

  B4 B5 A0215

  structural biology

  Computational Bioscience Research Center (CBRC) is pleased to announce the KAUST Research Conference 2023 on Computational Advances in Structural Biology. The conference will be held from May 1-3, 2023 at the Auditorium between Building 4 & 5. The visualization of the structure of a molecule, organelle or larger entity is key to understanding its biological function. Thus, structural biology is an extremely powerful means of unraveling the fundaments of life, but also of diseases. Consequently, structural biology is at the heart of medical therapies, including drug design, and has enabled the

Apr 23 - May 7, 2023

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  Multimodal Video Analysis

  Juan Carlos L. Alcazar, Postdoctoral Research Fellow, Image and Video Understanding Lab

  Apr 30, 12:00 - 13:00

  B9 L2 H2

  Multimodal Video Analysis

  The recognition of human behavior has driven most advances in video analysis over the past decade.

Apr 16 - Apr 23, 2023

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  CT-based COVID-19 Diagnosis and Prognosis through Novel AI Model Development

  Xin Gao, Program Chair, Computer Science

  Apr 18, 16:00 - 17:00

  B2 L5 R5220

  CT-based COVID-19 Diagnosis Prognosis novel methodology model development

  During the recent pandemic, urgent advances have been made by the scientific community in developing artificial intelligence (AI)-based computer-aided systems for CT-based COVID-19 diagnosis. In this talk, I will introduce our work on a fully-automatic, rapid, accurate, and machine-agnostic method that can segment and quantify the infection regions on CT scans from different sources.
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  Latent Space Manipulation of GANs for Seamless Image Compositing

  Anna Fruehstueck, Ph.D., Computer Science

  Apr 17, 17:30 - 18:30

  B5 L5 R5220

  Generative Adversarial Networks image synthesis texture synthesis

  Generative Adversarial Networks (GANs) are a very successful method for high-quality image synthesis and are a powerful tool to generate realistic images by learning their visual properties from a dataset of exemplars. However, the controllability of the generator output still poses many challenges. In this thesis, we propose several methods for achieving larger and/or higher visual quality in GAN outputs by combining latent space manipulations with image compositing operations
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  Pore-scale modeling of flow and transport in deformable porous media

  S. Majid Hassanizadeh, Professor, Stuttgart University, Germany, Utrecht University, The Netherlands,

  Apr 17, 12:00 - 13:00

  B9 L3 R3128

  Pore-scale models are valuable tools for the investigation of various flow and transport processes in porous media, and upscaling from pore to core scales. Popular approaches include pore-network models (PNM), Lattice-Boltzman models (LBM), Smooth Particle Hydrodynamics (SPH), volume-of-fluid method (VOF), and grain-scale models (GSM).