Events

May 8 - May 14, 2022

• 

  Introduction to Computer Simulation - A short course - Lecture 3

  Michal A. Mankowski, Assistant Professor, Erasmus School of Economics, Erasmus University Rotterdam, Netherlands

  May 10, 10:00 - 11:30

  B1 L4 R4102

  Computer simulations

  This course aims to familiarize students with the 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. Later in the course, the concept of simulation-based optimization will be discussed, introducing an overview of various optimization approaches. The example of simulation (and optimization) applied to design an optimal organ allocation policy in the US will be discussed. The last lecture will be devoted to the contemporary topics in simulation.
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  Software challenges in the renewable hydrogen economy

  Mani Sarathy, Professor, Chemical Engineering

  May 9, 12:00 - 13:00

  KAUST

  Hydrogen is a carbon-free energy carrier that can be used to decarbonize various high-emitting sectors, such as transportation, power generation, and industry. Today, global hydrogen production is largely derived from fossil fuels such as natural gas and coal.
• 

  Introduction to Computer Simulation - A short course - Lecture 2

  Michal A. Mankowski, Assistant Professor, Erasmus School of Economics, Erasmus University Rotterdam, Netherlands

  May 9, 10:00 - 11:30

  B1 L4 R4102

  Computer simulations

  This course aims to familiarize students with the 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. Later in the course, the concept of simulation-based optimization will be discussed, introducing an overview of various optimization approaches. The example of simulation (and optimization) applied to design an optimal organ allocation policy in the US will be discussed. The last lecture will be devoted to the contemporary topics in simulation.
• 

  Introduction to Computer Simulation - A short course - Lecture 1

  Michal A. Mankowski, Assistant Professor, Erasmus School of Economics, Erasmus University Rotterdam, Netherlands

  May 8, 10:00 - 11:30

  B1 L4 R4102

  Computer simulations

  This course aims to familiarize students with the 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. Later in the course, the concept of simulation-based optimization will be discussed, introducing an overview of various optimization approaches. The example of simulation (and optimization) applied to design an optimal organ allocation policy in the US will be discussed. The last lecture will be devoted to the contemporary topics in simulation.

Apr 24 - Apr 30, 2022

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  Private Learning with Heavy-tailed Data

  Di Wang, Assistant Professor, Computer Science

  Apr 25, 12:00 - 13:00

  B9 R2322 H1

  Differential Privacy (DP) allows for rich statistical and machine learning analysis, and is now becoming a gold standard for private data analysis. Despite the noticeable success of this theory, existing tools from DP are severely limited to regular datasets, e.g., datasets need to be or are assumed to be clean and normalized before performing DP algorithms.

Apr 17 - Apr 23, 2022

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  Electric Power to the People: Sustainable, Secure, and Resilient Energy Systems

  Charalambos (Harrys) Konstantinou, Associate Professor, Electrical and Computer Engineering

  Apr 18, 12:00 - 13:00

  B9 R2322 H1

  The power system is facing unprecedented changes in operation and control as more and diverse sources and loads are being connected to this complex cyber-physical energy system.

Apr 10 - Apr 16, 2022

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  Geospatial Data Science for Public Health Surveillance

  Apr 11, 12:00 - 13:00

  B9 R2322 H1

  Geospatial health data are essential to inform public health and policy. These data can be used to quantify disease burden, understand geographic and temporal patterns, identify risk factors and measure inequalities. In this talk, I will give an overview of statistical methods and computational tools for geospatial data analysis and health surveillance.

Apr 3 - Apr 9, 2022

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  Learning-Based Approaches for Next-Generation Intelligent Networks

  Liang Zhang, Ph.D. Student, Electrical and Computer Engineering

  Apr 5, 14:00 - 16:00

  KAUST

  communication network problems communication networks

  In this thesis, we investigate how learning-based approaches are implemented to solve the communication network problems and how communication network dependencies impact the training of learning-based approaches.
• 

  Visualization for DNA Nanotechnology Design and Simulations

  Ivan Viola, Professor, Computer Science

  Apr 4, 12:00 - 13:00

  B9 R2322 H1

  DNA Nanotechnology is a fascinating field that studies how to construct small biological structures entirely from DNA as a building material. The key insight is that DNA, if designed in a particular way, can construct complex 3D nanoscale structures entirely by means of self-assembly, governed by the base-pairing principle.

Mar 27 - Apr 2, 2022

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  To Encourage or to Restrict: the Label Dependency in Multi-Label Learning

  Zhuo Yang, Ph.D. Student, Computer Science

  Mar 30, 17:30 - 19:30

  KAUST

  machine learning data mining

  Multi-label learning addresses the problem that one instance can be associated with multiple labels simultaneously. More or less, these labels are usually dependent on each other in different ways. Understanding and exploiting the Label Dependency (LD) is well-accepted as the key to build high-performance multi-label classifiers, i.e., classifiers having abilities including but not limited to generalizing well on clean data and being robust under evasion attack.
• 

  Omics-based Metastasis Prediction using Machine Learning and Deep Learning

  Somayah Albaradei, Ph.D. Student, Computer Science

  Mar 30, 14:00 - 15:00

  KAUST

  bioinformatics Computational biology machine learning Computer Vision

  Knowing metastasis is the primary cause of cancer-related deaths incentivized research to unravel the complex cellular processes that drive the metastasis. Advancement in technology and specifically the advent of high-throughput sequencing provides knowledge of such processes. This knowledge led to the development of therapeutic and clinical applications. In this regard, predicting metastasis onset has also been explored using artificial intelligence (AI) approaches that are machine learning (ML), and more recently, deep learning (DL).
• 

  Masterclass on Visual Computing

  Dominik Michels, Helmut Pottmann, Ivan Viola, Peter Wonka, Soeren Pirk, Wolfgang Heidrich

  Mar 29, 14:30 - Mar 30, 17:15

  KAUST

  visual computing

  Visual Computing has become a key enabling technology for a diverse set of applications spanning scientific discovery, digital services, medicine, robotics, consumer electronics, and entertainment, to name just a few. The research community tackles problems in this vast space by drawing from expertise in multiple disciplines, including Computer Science, Electrical Engineering, and Mathematics. The KAUST Masterclass on Visual Computing highlights a selection of cutting-edge academic research within this field by comprising a series of talks focusing on different topics ranging from Computational Architecture and Fabrication, Deep Optics, and Generative Modeling, to Nanovisualization, Physics-based simulation, and Representation Learning.
• 

  Energy Efficient Neuromorphic computing systems

  Khaled Salama, Professor, Electrical and Computer Engineering

  Mar 28, 12:00 - 13:00

  B9 R2322 H1

  Traditional computing systems separate processors from memory, performing computation by shuttling data back and forth between these two units all the time. This bottleneck incurs limited processing speed and high power consumption in computing systems for deep learning models of ever-increasing complexity. Novel approaches and new principles are needed to revolutionize computing systems. Neuromorphic systems are proposed as a new computing architecture based on spiking neural networks analogous to the existing nervous systems.

Mar 20 - Mar 26, 2022

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  Permutation compressors for provably faster distributed nonconvex optimization

  Peter Richtarik, Professor, Computer Science

  Mar 21, 12:00 - 13:00

  B9 R2322 H1

  We study the MARINA method of Gorbunov et al (ICML 2021) - the current state-of-the-art distributed non-convex optimization method in terms of theoretical communication complexity. Theoretical superiority of this method can be largely attributed to two sources: the use of a carefully engineered biased stochastic gradient estimator, which leads to a reduction in the number of communication rounds, and the reliance on {\em independent} stochastic communication compression operators, which leads to a reduction in the number of transmitted bits within each communication round.

Mar 13 - Mar 19, 2022

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  Making Autonomous Driving more Reliable using Live 3D Digital Twins

  Fawad Ahmad, PhD Candidate, Computer Science Department, University of Southern California

  Mar 14, 18:30 - 19:30

  KAUST

  A live digital twin is a high-fidelity 3D representation of a physical object. This digital representation continuously replicates the physical object in real-time. My research vision is to build a live digital twin of the entire world. A live digital twin creates unprecedented capabilities for both computer and human consumption. It has the potential to improve safety and efficiency for autonomous driving, monitor on-going construction, and enable timely disaster relief operations etc. For humans, it means the possibility of digitally transporting to any place on the globe to live, interact and experience it in 3D like never before. These capabilities have strict performance and accuracy requirements. Achieving these requirements is not possible today for two reasons: limited wireless bandwidths, and limited on-board compute resources. I will talk about how I have tackled these challenges in my research to build end-to-end systems that build live digital twins and consume them for safer and more reliable autonomous driving. I will also discuss how I plan to implement my vision for building a live digital twin of the world in future.
• 

  Towards Secure and Scalable Blockchains

  Jiangshan Yu, Senior Lecturer, Monash University, Australia

  Mar 13, 12:00 - 13:00

  B2 B3 A0215

  blockchain

  Over the last decade, we have witnessed a rapid growth of blockchain technologies and their applications. Different governments have announced their strategy to boost the blockchain industry. For example, Chinese President Xi Jinping has recently endorsed blockchain and its potential for the Chinese economy; and the Australian Government announced that it would establish a National Blockchain Roadmap to help position Australia’s blockchain industry to become a global leader. This talk presents an overview of three of our works towards secure and scalable blockchains. I will first revisit the honest majority assumption of permissionless blockchains (AsiaCCS’21), and then present our efforts in making blockchain more scalable and secure against real-world threats.
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  Resilient operation of future low-carbon cyber-physical power grids

  Fei Teng, Lecturer, Electrical and Electronic Engineering

  Mar 13, 12:00 - 13:00

  B9 L2 R2322 H1

  resilient cyber-physical

  Intelligent monitoring, communication and control have been widely used in power systems to facilitate the cost-effective integration of renewable energy to achieve decarbonization.
• 

  Rising Stars in AI Symposium 2022 at KAUST

  Jürgen Schmidhuber, Professor, Computer Science

  Mar 13, 09:00 - Mar 15, 17:00

  B4 B5 A0215

  artificial intelligence machine learning mathematical optimization Deep Reinforcement Learning Computer Vision Natural Language Processing

  Rising Stars in AI Symposium 2022 at KAUST The "Rising Stars in AI Symposium" is geared towards young researchers (including PhD students, PostDocs, young faculty) who have recently published promising work at leading venues. There will be dozens of brief in-person presentations about papers recently accepted at major AI conferences such as NeurIPS, CVPR, EMNLP, ACL, ICML, ICLR, etc. All speakers will be encouraged to start with an intro for non-AI experts. To view the complete program and for more event details, please visit the symposium website. Attend the Symposium The symposium will be

Feb 27 - Mar 5, 2022

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  Filling the Resources Gap with Approximate Mobile Computing

  Veljko Pejović, Assistant professor at the Faculty of Computer and Information Science (UL FRI), University of Ljubljana, Slovenia

  Feb 28, 12:00 - 13:00

  B9 R2322 H1

  Mobile computing proliferation is critically threatened by the breakdown of Dennard scaling, a law describing the area-proportional growth of integrated circuit power use.

Feb 20 - Feb 26, 2022

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  Computer Algebra in Scientific Computing

  Dmitry Lyakhov, Research Scientist, Applied Mathematics and Computational Sciences

  Feb 21, 12:00 - 13:00

  B9 R2322 H1

  In this talk I will speak about some recent theoretical advances in symbolic computation. In particular, exact computations with polynomials and differential equations will be discussed.
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  When Second Order Methods Shine: Big Batches, Bayes, and Bilevel

  Guodong Zhang, PhD student in University of Toronto

  Feb 20, 15:00 - 16:00

  KAUST

  In this talk, I will discuss how the use of second-order information – e.g, curvature or covariance – can help in all three problems, yet with vastly different roles in each. First, I will present a noisy quadratic model, which qualitatively predicts scaling properties of a variety of optimizers and in particular suggests that second-order optimization algorithms would extend perfect scaling to much bigger batches. Second, I will show how we can derive and implement scalable and flexible Bayesian inference algorithms from standard second-order optimization algorithms. Third, I will describe a novel second-order algorithm that finds desired equilibria and save us from converging to spurious fixed points in two-player sequential games (i.e. bilevel optimization) or even more general settings. Finally, I will conclude how my research would pave the way towards intelligent machines that can learn from data and experience efficiently, reason about their own decisions, and act in our interests.

Feb 13 - Feb 19, 2022

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  Security and Resiliency for Critical Infrastructures

  Professor Roberto Di Pietro, College of Science and Engineering, Cybersecurity at Hamad Bin Khalifa University

  Feb 14, 12:00 - 13:00

  B9 L2 R2322 H1

  Our standard of living, nation GDP, and, in some cases, even our safety rely on critical infrastructures (CIs). In particular, being CIs generally perceived as a commodity (think of GPS availability, or avionics & maritime traffic routes and hubs), their security has largely been overlooked. The emergent property is that, nowadays, CIs systems are generally fragile, especially with respect to cyber attacks.

Feb 6 - Feb 12, 2022

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  Procedural Modeling of Botanical Trees

  Wojtek Palubicki is leading the Natural Phenomena Modeling Group at the Faculty of Mathematics and Compter Science at the Adam Mickiewicz University in Poznan.

  Feb 7, 12:00 - 13:00

  KAUST

  The exact mechanisms controlling plant growth are a topic of ongoing botanical research. This makes the realistic computer simulation of plants a challenging task. We present a method for generating realistic models of trees and shrubs. This method is based on the biological hypothesis that the form of a developing tree emerges from a self-organizing process dominated by the competition of buds and branches for light or space, and regulated by internal signaling mechanisms. Simulations of this process robustly generate a wide range of realistic trees and bushes.
• 

  Safe and Secure Computer Control Systems? The devil is in the details...

  Feb 6, 12:00 - 13:00

  B9 L2 R2322 H1

  In modern societies, there is an accelerated convergence of information technology (IT) like the internet-cloud-web complex, with operational technology (OT) like cyber-physical systems (CPS) control, complemented by Internet-of-Things fabrics (IoT).

Jan 30 - Feb 5, 2022

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  Exoplanet Imaging Coronagraphic Mask Development and Testing: A North American Perspective

  Benjamin L. Gerard, Postdoctoral Scholar, University of California, UC Observatories

  Feb 2, 16:30 - 17:30

  KAUST

  Over 4000 exoplanets - planets beyond the Solar System - have been discovered since the first Nobel prize-winning exoplanet detection around a Sun-like star in 1995. The majority of these exoplanets have been detected by indirect methods, inferring the presence of the exoplanet by observing the star.
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  New tools for symbolic computation in partial differential equations

  Diogo Gomes, Program Chair, Applied Mathematics and Computational Sciences

  Jan 31, 12:00 - 13:00

  KAUST

  The qualitative study of PDEs often relies on integral identities and inequalities. For example, for time-dependent PDEs, conserved integral quantities or quantities that are dissipated play an important role. In particular, if these integral quantities have a definite sign, they are of great interest as they may provide control on the solutions to establish well-posedness.

Jan 23 - Jan 29, 2022

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  Extensions of Dynamic Programming for Combinatorial Optimization and Data Mining - 2022-01-24

  Mikhail Moshkov, Professor, Applied Mathematics and Computational Sciences

  Jan 24, 12:00 - 13:00

  KAUST

  Dynamic programming is an efficient technique to solve optimization problems. It is based on decomposing the initial problem into simpler ones and solving these sub-problems beginning from the simplest ones.

Dec 5 - Dec 11, 2021

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  Deploying DNNs in the Embedded Space: Challenges and Opportunities

  Christos-Savvas Bouganis, Reader in Intelligent Digital Systems in the Department of Electrical and Electronic Engineering, Imperial College London, UK

  Dec 6, 12:00 - 13:00

  B9 R2322 H1

  The talk will discuss the challenging problem of designing Deep Neural Network systems that achieve high performance under low power envelopes, hindering their deployment in the embedded space.

Nov 28 - Dec 4, 2021

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  From Intrusion Tolerance to Trust and Recovery: A Research Overview

  Professor Miguel Correia, Computer Science and Engineering Department, Instituto Superior Técnico, Universidade de Lisboa

  Dec 1, 12:00 - 13:00

  B9 L3 R3223

  The seminar is an overview on my research path and approach. I will start with my initial research on the concept of intrusion tolerance and present some of the results I had at the time. Then, I will show how this initial research had led me to other areas and related approaches: software security, trusted computing, intrusion detection, intrusion recovery, and blockchain interoperability. I will present with some detail very recent research on securely moving data between trusted execution environments (TEEs) in a non-interactive way, a problem that must be solved for digital id crypto wallets starting to appear in Europe and elsewhere.
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  DeepReduce: A Sparse-tensor Communication Framework for Distributed Deep Learning

  Panagiotis Kalnis, Professor, Computer Science

  Nov 29, 12:00 - 13:00

  B9 R2322 H1

  Sparse tensors appear frequently in distributed deep learning, either as a direct artifact of the deep neural network's gradients, or as a result of an explicit sparsification process. Most communication primitives are agnostic to the peculiarities of deep learning; consequently, they impose unnecessary communication overhead.
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  Secure Dynamic Skyline Queries Using Result Materialization

  Gabriel Ghinita, Associate Professor, University of Massachusetts, Boston

  Nov 28, 12:00 - 13:00

  B9 L2 R2322

  Skyline computation is an increasingly popular query, with broad applicability to many domains. Given the trend to outsource databases, and due to the sensitive nature of the data (e.g., in healthcare), it is essential to evaluate skylines on encrypted datasets.

Nov 21 - Nov 27, 2021

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  Knowledge-based methods in bioinformatics

  Robert Hoehndorf, Associate Professor, Computer Science

  Nov 22, 12:00 - 13:00

  B9 R2322 H1

  The life sciences have invested significant resources in the development and application of semantic technologies to make research data accessible and interlinked, and to enable the integration and analysis of data. Utilizing the semantics associated with research data in data analysis approaches is often challenging. Now, novel methods are becoming available that combine symbolic methods and statistical methods in Artificial Intelligence. In my talk, I will show how to incorporate biological background knowledge in machine learning models for identification of gene-disease associations, genomic variants that are causative for heritable disorders, and to predict protein functions. The methods I describe are generic and can be applied in other domains in which biomedical ontologies and structured knowledge bases exist.

Nov 14 - Nov 20, 2021

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  Discrete geometric structures for architecture and and computational fabrication

  Nov 15, 12:00 - 13:00

  B9 R2322 H1

  ‌ Abstract Discrete differential geometry aims to develop discrete equivalents of notions and methods from smooth surface theory. It constitutes an area of active research in mathematics, but also provides effective methodology for a variety of applications. This talk will illustrate the fruitful interplay between theory and applications of discrete differential geometry at hand of selected topics in architectural geometry and computational fabrication. We discuss the construction of architectural freeform skins from flat, nearly rectangular panels, and associated support structures. These
• 

  Towards Secure and Resilient Cyber-Physical Energy Systems - 2021-11-14

  Charalambos (Harrys) Konstantinou, Associate Professor, Electrical and Computer Engineering

  Nov 14, 12:00 - 13:00

  KAUST

  This talk will give an overview of the research of the Secure Next Generation Resilient Systems (SENTRY) lab (sentry.kaust.edu.sa) at KAUST.

Nov 7 - Nov 13, 2021

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  Lessons Learned In Building Trustworthy Systems with Trusted Execution Environments

  Valerio Schiavoni, Scientific Coordinator and Lecturer, Centre of Competence for Complex Systems and Big Data, University of Neuchâtel

  Nov 11, 12:00 - 13:00

  B9 L3 R3223

  Available as dedicated hardware components into several mobile and server-grade processors, and recently included in infrastructure-as-a-service commercial offerings by several cloud providers, TEEs allow applications with high privacy and confidentiality demands to be deployed and executed over untrusted environments, shielding data and code from compromised systems or powerful attackers. After an introduction to basic concepts for TEEs, I will survey some of our most recent contributions exploiting TEEs, including as defensive tools in the context of Federated Learning, as support to build secure cache systems for edge networks, as protection mechanisms in a med-tech/e-health context, shielding novel environments (ie, WebAssembly), and more. Finally, I will highlight some of the lessons learned and offer open perspectives, hopefully useful and inspirational to future researchers and practitioners entering this exciting area of research.
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  BioAI – Facts, Fiction, and the Future

  Jesper Tegner, Professor, Computer Science, KAUST

  Nov 8, 12:00 - 13:00

  B9 R2322 H1

  The natural sciences such as biology, medicine, and chemistry are currently in a transformative stage. Progress in technologies for measuring and collecting data (sequences, images, and molecules) has exploded since the human genome project. In parallel, we have witnessed stunning advances in what can broadly be referred to as computational techniques. This includes data-driven analysis of data such as Machine learning and Artificial Intelligence. From an ML/AI standpoint, there is a renewed interest in classical” equation-based modeling, causal analysis, and generative probabilistic modeling techniques. BioAI refers to this “perfect storm” between Bio and AI.

Oct 31 - Nov 6, 2021

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  Towards Generalized and Robust Knowledge Association

  Shichao Pei, Ph.D. Student, Computer Science

  Nov 2, 16:00 - 18:00

  KAUST

  Human knowledge can facilitate the evolution of artificial intelligence towards learning the capability of planning and reasoning and has been the critical element for developing the next-generation artificial intelligence. Although knowledge collection and organization have achieved significant progress, it is still non-trivial to construct a comprehensive knowledge graph for downstream applications. The difficulty motivates the study of knowledge association to resolve the problem, yet current solutions suffer from two primary shortages, i.e., generalization and robustness. Specifically, most existing methods require a sufficient number of labeled data and ignore the effective utilization of complex relationships between entities, limiting the generalization ability of knowledge association approaches. Moreover, prevailing approaches severely rely on clean labeled data, making the model vulnerable to noises in the given labeled data. These shortages motivate the research on generalization and robustness of knowledge association in this dissertation.
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  EF21: A new, simpler, theoretically better, and practically faster error feedback

  Peter Richtarik, Professor, Computer Science

  Nov 1, 12:00 - 13:00

  B9 R2322 H1

  Error feedback (EF), also known as error compensation, is an immensely popular convergence stabilization mechanism in the context of distributed training of supervised machine learning models enhanced by the use of contractive communication compression mechanisms, such as Top-k. First proposed by Seide et al (2014) as a heuristic, EF resisted any theoretical understanding until recently [Stich et al., 2018, Alistarh et al., 2018].

Oct 24 - Oct 30, 2021

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  The Impact of Information Technology on the Future of Energy Consumption

  Elmootazbellah Elnozahy, Professor (former), Computer Science

  Oct 25, 12:00 - 13:00

  B9 R2322 H1

  The human race is facing what may turn out to be an existential threat due to entrenched practices that are contributing to climate change. This talk addresses the impact of information technology (IT) in this regard.

Oct 10 - Oct 16, 2021

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  The Data-sparse Renaissance in Numerical Linear Algebra

  David Keyes, Senior Associate to the President, King Abdullah University of Science and Technology

  Oct 11, 12:00 - 13:00

  B9 R2322 H1

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

Oct 3 - Oct 9, 2021

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  On Seven Fundamental Optimization Challenges in Machine Learning

  Konstantin Mishchenko, Ph.D. Student, Computer Science

  Oct 4, 17:00 - 18:00

  KAUST

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

  Ricardo Pérez-Marco, Visiting Professor at KAUST, CNRS researcher in Paris

  Oct 4, 12:00 - 13:00

  B9 R2322 H1

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

Sep 26 - Oct 2, 2021

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  Towards Secure and Resilient Cyber-Physical Energy Systems - 2021-09-27

  Charalambos Konstantinou, Assistant Professor, Computer Science, Electrical and Computer Engineering

  Sep 27, 12:00 - 13:00

  B9 R2322 H1

  This talk will give an overview of the research of the Secure Next Generation Resilient Systems (SENTRY) lab (sentry.kaust.edu.sa) at KAUST. The transformation of critical infrastructures into cyber-physical systems contributes towards modernization allowing for better planning, more flexible control, system-wide optimization, etc. The security, however, of such systems presents significant challenges in controlling and maintaining secure access to critical system resources and services.

Sep 19 - Sep 25, 2021

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  Deep Optics - Joint Design of Imaging Hardware and Reconstruction Methods - Graduate Seminar

  Wolfgang Heidrich, Professor, Computer Science

  Sep 20, 12:00 - 13:00

  B9 R2322 H1

  Classical imaging systems are characterized by the independent design of optics, sensors, and image processing algorithms. In contrast, computational imaging systems are based on a joint design of two or more of these components, which allows for greater flexibility of the type of captured information beyond classical 2D photos, as well as for new form factors and domain-specific imaging systems. In this talk, I will describe how numerical optimization and learning-based methods can be used to achieve truly end-to-end optimized imaging systems that outperform classical solutions.
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  Resilience is the Cybersecurity of the XXI century, and here's why! - 2021-09-19

  Sep 19, 12:00 - 13:00

  KAUST

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

Sep 12 - Sep 18, 2021

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  A Journey in the Underwater Systems - Graduate Seminar

  Basem Shihada, Professor, Computer Science

  Sep 13, 12:00 - 13:00

  B9 R2322 H1

  In this seminar, I will go over our journey in the underwater networks research work. Basically, I will highlight our recent work on bringing the Internet to underwater environments by deploying a low power and compact underwater optical wireless system, called Aqua-Fi, that support today’s Internet applications.

Sep 5 - Sep 11, 2021

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  Computational Imaging and Its Applications in Fluids

  Jinhui Xiong, Ph.D. Student, Computer Science

  Sep 6, 16:00 - 17:00

  KAUST

  Computational imaging differs from traditional imaging systems by integrating an encoded measurement system and a tailored computational algorithm to extract interesting scene features. This dissertation demonstrates two approaches which apply computational imaging methods to the fluid domain. In the first approach, we study the problem of reconstructing time-varying 3D-3C fluid velocity vector fields. We extend 2D Particle Imaging Velocimetry to three dimensions by encoding depth into color.
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  A Toolkit for Multi-Faceted Geospatial Data Protection in Transactional and Analytical Processing using Searchable Encryption and Differential Privacy

  Gabriel Ghinita, Associate Professor, University of Massachusetts, Boston

  Sep 6, 12:00 - 13:00

  B9 R2322 H1

  The mobile revolution of the past decade led to the ubiquitous presence of location data in all application domains, ranging from public safety and healthcare to urban planning, transportation and commercial applications. Numerous services rely on location data to provide customized service to their users. At the same time, there are serious concerns with respect to protecting individual privacy, as location traces can disclose sensitive details to an untrusted service.

Aug 29 - Sep 4, 2021

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  Large-Scale Visualization of Scientific Data: Volume Data and Flow Fields - 2021-08-30

  Markus Hadwiger, Professor, Computer Science

  Aug 30, 12:00 - 13:00

  B9 R2322 H1

  This talk will give an overview of the research of the High-Performance Visualization research group (vccvisualization.org) at the KAUST Visual Computing Center (VCC). Interactive visualization is crucial to exploring, analyzing, and understanding large-scale scientific data, such as the data acquired in medicine or neurobiology using computed tomography or electron microscopy, and data resulting from large-scale simulations such as fluid flow in the Earth’s atmosphere and oceans. The amount of data in data-driven science is increasing rapidly toward the petascale and further.

Aug 8 - Aug 14, 2021

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  Stylistic and spatial disentanglement in GANs

  Yazeed Alharbi, Ph.D., Computer Science

  Aug 12, 14:00 - 16:00

  KAUST

  Computer Vision machine learning generative adversarial network Deep learning

  This dissertation tackles the problem of entanglement in Generative Adversarial Networks (GANs). The key insight is that disentanglement in GANs can be improved by differentiating between the content, and the operations performed on that content. For example, the identity of a generated face can be thought of as the content, while the lighting conditions can be thought of as the operations.