Events

Dec 4 - Dec 10, 2022

• 

  Reynolds-robust preconditioners for the stationary incompressible Navier--Stokes equations

  Prof.Patrick Farrell, University of Oxford

  Dec 5, 12:00 - 13:00

  B9 L2 R2322 H1

  Navier-Stokes equation Robustness

  Building on the work of Schöberl, Olshanskii, and Benzi, in this talk we present the first preconditioner for the Newton linearization of the stationary Navier--Stokes equations in three dimensions that achieve both optimal complexity in of count and Reynolds-robustness. The exact details of the preconditioner varies with discretization, but the general theme is to combine augmented Lagrangian stabilisation, a custom multigrid prolongation operator involving local solves on coarse cells, and an additive patchwise relaxation on each level that captures the kernel of the divergence operator.

Nov 27 - Dec 3, 2022

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  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.

Nov 20 - Nov 26, 2022

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  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

Nov 13 - Nov 19, 2022

• 

  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.

Nov 6 - Nov 12, 2022

• 

  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.
• 

  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.
• 

  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.

Oct 30 - Nov 5, 2022

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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.

Oct 23 - Oct 29, 2022

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  Applied AI for sustainable development

  Prof.Evgeny Burnaev, Applied AI Center, Skolkovo Institute of Science and Technology

  Oct 24, 12:00 - 13:00

  B9 L2 R2322 H1

  Skoltech machine learning Bayesian modeling artificial intelligence

  Skoltech Applied AI center’s mission is to create AI models and frameworks for solving the problems of sustainable development of industry and economy. In my presentation, I will overview the current center's activities, applied and fundamental problem statements, and corresponding recent results.

Oct 16 - Oct 22, 2022

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  Inference for Longitudinal Data After Adaptive Sampling

  Prof. Susan Murphy, Statistics and Computer Science and Radcliffe Alumnae Professor at the Radcliffe Institute, Harvard University

  Oct 20, 15:00 - 16:00

  B9 L2 H2

  Reinforcement Learning

  In this work, we proved statistical inference for the common Z-estimator based on adaptively sampled data. Adaptive sampling methods, such as reinforcement learning (RL) and bandit algorithms, are increasingly used for the real-time personalization of interventions in digital applications like mobile health and education. As a result, there is a need to be able to use the resulting adaptively collected user data to address a variety of inferential questions, including questions about time-varying causal effects.
• 

  We used Reinforcement Learning; but did it work?

  Prof. Susan Murphy, Statistics and Computer Science and Radcliffe Alumnae Professor at the Radcliffe Institute, Harvard University

  Oct 19, 16:00 - 17:00

  B9 L2 H2

  Reinforcement Learning Digital Behavioral Health

  Reinforcement Learning provides an attractive suite of online learning methods for personalizing interventions in Digital Behavioral Health. However, after a reinforcement learning algorithm has been run in a clinical study, how do we assess whether personalization occurred? We might find users for whom it appears that the algorithm has indeed learned in which contexts the user is more responsive to a particular intervention. But could this have happened completely by chance? We discuss some first approaches to addressing these questions.

Oct 9 - Oct 15, 2022

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  EF-BV: distributed optimization with compressed communication

  Laurent Condat, Senior Research Scientist, Computer, Electrical and Mathematical Sciences and Engineering

  Oct 10, 12:00 - 13:00

  B9 L2 R2322 H1

  big data distributed computing Federated learning

  In the big data era, it is necessary to rely on distributed computing. For distributed optimization and learning tasks, in particular in the modern paradigm of federated learning, specific challenges arise, such as decentralized data storage. Communication between the parallel machines and the orchestrating distant server is necessary but slow. To address this main bottleneck, a natural strategy is to compress the communicated vectors. I will present EF-BV, a new algorithm which converges linearly to an exact solution, with a large class of deterministic or random, biased or unbiased compressors.

Oct 2 - Oct 8, 2022

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  Towards Structured Intelligence with Deep Graph Neural Networks

  Guohao Li, Ph.D., Computer Science

  Oct 5, 18:00 - 20:00

  B5 L5 R5209

  graph machine learning Graph Neural Networks

  This dissertation discusses approaches to building large-scale and efficient graph machine learning models for learning structured representation with applications to engineering and sciences. This work would present how to make Graph Neural Networks (GNNs) go deep by introducing architectural designs and how to automatically search GNN architectures by novel neural architecture search algorithms.
• 

  A new class of random field models for data on networks

  David Bolin, Professor, Statistics

  Oct 3, 12:00 - 13:00

  B9 L2 R2322 H1

  applied statistics machine learning Gaussian processes

  Random fields are popular models in statistics and machine learning for spatially dependent data on Euclidian domains. However, in many applications, data is observed on non-Euclidian domains such as street networks. In this case, it is much more difficult to construct valid random field models. In this talk, we discuss some recent approaches to modeling data in this setting, and in particular define a new class of Gaussian processes on compact metric graphs.

Sep 25 - Oct 1, 2022

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  Designing High-Performance and Scalable Middleware for HPC, AI, and Data Science

  Prof. Dhabaleswar K. (DK) Panda, Professor, Computer Science and Engineering, The Ohio State University

  Sep 26, 12:00 - 13:00

  B9 L2 R2322 H1

  data science Deep learning machine learning artificial intelligence

  This talk will focus on challenges and opportunities in designing middleware for HPC, AI (Deep/Machine Learning), and Data Science. We will start with the challenges in designing runtime environments for MPI+X programming models by considering support for multi-core systems, high-performance networks (InfiniBand and RoCE), GPUs, and emerging BlueField-2 DPUs. Features and sample performance numbers of using the MVAPICH2 libraries will be presented. For the Deep/Machine Learning domain, we will focus on MPI-driven solutions to extract performance and scalability for popular Deep Learning frameworks (TensorFlow and PyTorch), large out-of-core models, and Bluefield-2 DPUs.

Sep 18 - Sep 24, 2022

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  Towards Detailed Understanding of the Visual World

  Fahad Khan, Associate Professor at MBZUAI and Linköping University

  Sep 19, 12:00 - 13:00

  B9 L2 R2322

  Computer Vision

  Machine perception that corresponds to the ability to understand the visual world based on the input from sensors, such as cameras is one of the central problems in Artificial Intelligence. To this end, recent years have witnessed tremendous progress in various instance-level recognition tasks having real-world applications in e.g., robotics, autonomous driving and surveillance. In this talk, I will first present our recent results towards understanding state-of-the-art deep learning-based visual recognition networks in terms of their robustness and generalizability. Next, I will present our results on learning visual recognition models with limited human supervision. Finally, I will discuss moving one step further from instance-level recognition to understand visual relationships between object pairs.

Sep 11 - Sep 17, 2022

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  Optimal Algorithms for Affinely Constrained, Distributed, Decentralized, Minimax, and High-Order Optimization Problems

  Dmitry Kovalev, Ph.D. Student, Computer Science

  Sep 14, 16:00 - 18:30

  B5 R5220

  optimization machine learning

  In this thesis, we discuss a few fundamental and well-studied optimization problem classes: decentralized distributed optimization (Chapters 2 to 4), distributed optimization under similarity (Chapter 5), affinely constrained optimization (Chapter 6), minimax optimization (Chapter 7), and high-order optimization (Chapter 8). For each problem class, we develop the first provably optimal algorithm: the complexity of such an algorithm cannot be improved for the problem class given. The proposed algorithms show state-of-the-art performance in practical applications, which makes them highly attractive for potential generalizations and extensions in the future.
• 

  Architecting the Future of Wireless Networks Connectivity

  Basem Shihada, Professor, Computer Science

  Sep 13, 14:00 - 15:30

  B9 L2 H2

  In this talk, I will start by providing our vision for next-generation networks. Throughout the talk, I will highlight several challenges in existing communication technologies that could have the potential of shaping new research and deployment directions of future wireless networks, including, (i) review our recent advances in non-terrestrial networks, which includes both UAVs and satellite (ii) show satellite systems are essential for today’s traffic-intensive applications while maintaining an accepted end-to-end latency for delay-sensitive applications and (iii) show how we integrated both existing Wi-Fi technology with optics to extend the Internet as we use it today to the underwater environments via Aqua-fi.
• 

  Defeating the attacks from bots scrapers hiding behind residential proxy IPs

  Marc Dacier, Professor, Computer Science

  Sep 12, 12:00 - 13:00

  B9 L2 R2322 H1

  operational security resilient computing

  This talks presents a very serious emerging threat: the bots scraping web sites and hiding their IPs thanks to residential IP providers. The problem, state of the art and a new solution will be explained.

Sep 4 - Sep 10, 2022

• 

  Communication-Efficient Distributed Newton-type Algorithms for Federated Learning

  Mher Safaryan, Postdoctoral Research Fellow, Applied Mathematics and Computational Sciences

  Sep 5, 12:00 - 13:00

  B9 L2 R2322 H1

  machine learning

  In this talk, I will discuss communication compression and aggregation mechanisms for curvature information in order to reduce these costs while preserving theoretically superior local convergence guarantees.
• 

  Resilience is the Cybersecurity of the XXI century, and here's why! - 2022-09-04

  Sep 4, 12:00 - 13:00

  B9 L2 R2322

  Resilience is the Cybersecurity of the XXI century

  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.

Aug 28 - Sep 3, 2022

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  Which came first: the paper or the peer-review?

  Marco Canini, Professor, Computer Science

  Aug 29, 12:00 - 13:00

  B9 L2 R2322 H1

  This talk presents some perspectives on doing research in computer science and publishing through the peer review process.

Jun 26 - Jul 2, 2022

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  Drug Repositioning through the Development of Diverse Computational Methods using Machine Learning, Deep Learning, and Graph Mining

  Maha Thafar, Ph.D. Student, Computer Science

  Jun 30, 08:30 - 10:30

  KAUST

  Computational biology machine learning Deep learning graph mining

  In this dissertation, we combined artificial intelligence and machine/deep learning with chemical and biological properties to develop several computational methods to solve biomedical domain problems, specifically drug repositioning, and demonstrated their efficiencies and capabilities. We developed three network-based DTI prediction methods using machine learning, graph embedding, and graph mining. These methods significantly improved prediction performance, and the best-performing method even reduces the error rate by more than 33% across all datasets compared to the best state-of-the-art method. As it is more insightful to predict continuous values that indicate how tightly the drug binds to a specific target, we conducted a comparison study of current regression-based methods that predict drug-target binding affinities (DTBA). Our methods demonstrated their efficiency and capability by achieving high prediction performance and identifying therapeutic targets for several cancer types. We further conducted a lung cancer case study of findings that support the novel predicted targets.
• 

  IoT via 5G and Beyond 5G Satellite Systems

  Prof. Giovanni Giambene, Information Engineering and Mathematics, University of Siena, Italy

  Jun 28, 12:50 - 14:00

  KAUST

  5G IoT Satellite Systems

  One of the key drivers for next-generation mobile communications is the support of the Internet of Things (IoT), with billions of objects connected to the Internet and very low latency. The 5G technology will support the realization of smart cities, smart environments, and big data applications.

Jun 19 - Jun 25, 2022

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  High-Performance Scientific Applications Using Mixed Precisions and Low-Rank Approximations Powered by Task-based Runtime Systems

  Rabab Alomairy, Postdoctoral Research Fellow, King Abdullah University of Science and Technology

  Jun 20, 11:00 - 13:00

  B9 L4 R4223

  Tile Low Rank Algorithmic redesign Task based Runtime Systems

  Scientific applications from diverse sources rely on dense matrix operations. These operations arise in: Schur complements, integral equations, covariances in spatial statistics, ridge regression, radial basis functions from unstructured meshes, and kernel matrices from machine learning, among others. This thesis demonstrates how to extend the problem sizes that may be treated and reduce their execution time. Sometimes, even forming the dense matrix can be a bottleneck – in computation or storage.

Jun 12 - Jun 18, 2022

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  How Can Data Science Change Everyday Life?

  Prof. Wadii Boulila, Computer Science, Prince Sultan University

  Jun 12, 16:00 - 17:00

  B1 L3 R3119

  This talk will present our recent research works related to the application of data science in the environment, healthcare, and social media.

May 29 - Jun 4, 2022

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  Critical Infrastructures and Satellites Security

  Roberto Di Pietro, Full Professor in Cybersecurity, Hamad Bin Khalifa University

  Jun 1, 08:30 - 09:30

  B9 L2 H2325

  Critical Infrastructures (CIs) are the cornerstone Economics and Society rely upon. In this talk, we will start with surveying some of the threats different CIs are subject to, highlighting some research problems and related results.

May 15 - May 21, 2022

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  It is Okay to not be Okay: Overcoming Emotional Bias in Affective Image Captioning by Contrastive Data Collection (ArtEmis 2.0)

  Mohamed Elhoseiny, Associate Professor, Computer Science

  May 16, 12:00 - 13:00

  B9 R2322 H1

  Datasets that capture the connection between vision, language, and affection are limited, causing a lack of understanding of the emotional aspect of human intelligence. As a step in this direction, the ArtEmis dataset was recently introduced as a large-scale dataset of emotional reactions to images along with language explanations of these chosen emotions.

May 8 - May 14, 2022

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  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.
• 

  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.
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  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.
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  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).
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  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.
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  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.
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  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.
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  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.