Events

Nov 7 - Nov 13, 2021

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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.

Jun 27 - Jul 3, 2021

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  Artificial Intelligence methods for precision health: Applications to rare disease and cancer research

  Robert Hoehndorf, Associate Professor, Computer Science

  Jun 27, 10:00 - 11:00

  B9 L2 H2

  artificial intelligence

  Abstract 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 the identification of gene-disease

Jun 13 - Jun 19, 2021

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  Reconfigurable Snapshot HDR Imaging Using Coded Masks

  Masheal Alghamdi, Ph.D., Computer Science

  Jun 17, 12:00 - 14:00

  KAUST

  High Dynamic Range (HDR) image acquisition from a single image capture, also known as snapshot HDR imaging, is challenging because the bit depths of camera sensors are far from sufficient to cover the full dynamic range of the scene. Existing HDR techniques focus either on algorithmic reconstruction or hardware modification to extend the dynamic range. In this thesis, we propose a joint design for snapshot HDR imaging by devising a spatially varying modulation mask in the hardware combined with a deep learning algorithm to reconstruct the HDR image. In this approach, we achieve a reconfigurable HDR camera design that does not require custom sensors, and instead can be reconfigured between HDR and conventional mode with very simple calibration steps. We demonstrate that the proposed hardware-software solution offers a flexible, yet robust, way to modulate per-pixel exposures, and the network requires little knowledge of the hardware to faithfully reconstruct the HDR image. Comparative analysis demonstrated that our method outperforms the state-of-the-art in terms of visual perception quality.

May 2 - May 8, 2021

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  Community Detection in Biological Networks: Lessons from the DREAM 2016 Module Identification Challenge

  Lenore J. Cowen is a Professor in the Computer Science Department at Tufts University

  May 3, 18:30 - 19:30

  KAUST

  The 2016 DREAM Disease Module Identification Challenge was developed to systematically assess the state of computational module identification methods on a diverse collection of molecular networks. Six different anonymized networks were presented with the gene names anonymized. The goal was to partition the genes into non-overlapping modules of from 3-100 genes each, based soley on the patterns of network connectivity.

Apr 25 - May 1, 2021

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  Energy-Efficiency and Security for EdgeAI: Challenges and Opportunities

  Muhammad Shafique , Professor, Division of Engineering, New York University Abu Dhabi (NYU-AD), United Arab Emirates

  Apr 26, 12:00 - 13:00

  KAUST

  Gigantic rates of data production in the era of Big Data, Internet of Thing (IoT), and Smart Cyber Physical Systems (CPS) pose incessantly escalating demands for massive data processing, storage, and transmission while continuously interacting with the physical world under unpredictable, harsh, and energy-/power-constrained scenarios. Therefore, such systems need to support not only the high-performance capabilities under tight power/energy envelop, but also need to be intelligent/cognitive and robust. This has given rise to a new age of Machine Learning (and, in general Artificial Intelligence) at different levels of the computing stack, ranging from Edge and Fog to the Cloud. In particular, Deep Neural Networks (DNNs) have shown tremendous improvement over the past years to achieve a significantly high accuracy for a certain set of tasks, like image classification, object detection, natural language processing, and medical data analytics. However, these DNN require highly complex computations, incurring huge processing, memory, and energy costs. To some extent, Moore’s Law help by packing more transistors in the chip.

Apr 18 - Apr 24, 2021

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  How do people explore virtual environments? Capturing and modeling visual behavior in VR

  Belen Masia, Associate Professor in the Computer Science Department at Universidad de Zaragoza

  Apr 19, 12:00 - 13:00

  KAUST

  Virtual Reality (VR) can dramatically change the way we create and consume content in areas of our everyday life, including entertainment, training, design, communication or advertising. Understanding how people explore immersive virtual environments is crucial for many applications in VR, such as designing content, developing new compression algorithms, or improving the interaction with virtual humans. In this talk, we will focus on how to capture and model visual behavior of users in virtual environments.

Apr 11 - Apr 17, 2021

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  Ingesting and Processing Data Efficiently for Machine Learning

  Ana Klimovic, Assistant Professor, Systems Group of the Computer Science Department, ETH Zurich.

  Apr 12, 12:00 - 13:00

  KAUST

  Machine learning applications have sparked the development of specialized software frameworks and hardware accelerators. Yet, in today’s machine learning ecosystem, one important part of the system stack has received far less attention and specialization for ML: how we store and preprocess training data. This talk will describe the key challenges for implementing high-performance ML input data processing pipelines.

Apr 4 - Apr 10, 2021

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  Challenges of Differentially Private Empirical Risk Minimization

  Di Wang, Assistant Professor, Computer Science

  Apr 5, 12:00 - 13:00

  KAUST

  Recent research showed that most of the existing machine learning algorithms are vulnerable to various privacy attacks. An effective way for defending these attacks is to enforce differential privacy during the learning process. As a rigorous scheme for privacy preserving, Differential Privacy (DP) has now become a standard for private data analysis. Despite its rapid development in theory, DP's adoption to the machine learning community remains slow due to various challenges from the data, the privacy models and the learning tasks. In this talk, I will give a brief introduction on DP and use the Empirical Risk Minimization (ERM) problem as an example and show how to overcome these challenges in DP model. Particularly, I will first talk about how to overcome the high dimensionality challenge from the data for Sparse Linear Regression in the local DP (LDP) model. Then, I will discuss the challenge from the non-interactive LDP model and show a series of results to reduce the exponential sample complexity of ERM. Next, I will present techniques on achieving DP for ERM with non-convex loss functions. Finally, I will discuss some future research along these directions.

Mar 28 - Apr 3, 2021

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  Toward Improving the Internet of Things: Quality of Service and Fault Tolerance Perspectives

  Maha Al-Aslani, PhD Student, Computer Science, KAUST

  Mar 31, 16:00 - 17:00

  KAUST

  In this thesis defense, I will explore the unique characteristics of IoT traffic and examine IoT systems. The work is motivated by the new capabilities offered by modern Software Defined Networks (SDN) and blockchain technology. We evaluate IoT Quality of Service (QoS) in traditional networking. We obtain mathematical expressions to calculate end-to-end delay, and dropping. Then, we analyze IoT traffic load and propose an intelligent edge that can identify volumetric traffic and address them in real-time using an instantaneous detection method for IoT applications (IDIoT). This approach can easily detect a large surge and potential variation in traffic patterns for an IoT application, which may contribute to safer and more efficient operation of the overall system. Our results provide insight into the advantages of an intelligent edge serving as a detection mechanism.
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  Multi-Scale GPU Resource Management for Deep Learning

  Mosharaf Chowdhury, Morris Wellman, Assistant Professor of CSE at the University of Michigan, Ann Arbor

  Mar 29, 18:30 - 19:30

  KAUST

  GPUs have emerged as a popular choice for deep learning. To deal with ever-growing datasets, it is also common to use multiple GPUs in parallel for distributed deep learning. Although achieving cost-effectiveness in these clusters relies on efficient sharing, modern GPU hardware, deep learning frameworks, and cluster managers are not designed for efficient, fine-grained sharing of GPU resources. In this talk, I will present our recent works on efficient GPU resource management, both within a single GPU and across many GPUs in a cluster for hyperparameter tuning, training, and inference. The common thread across all our works is leveraging the interplay between short-term predictability and long-term unpredictability of deep learning workloads

Mar 21 - Mar 27, 2021

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  Automated Reasoning in Systems Engineering

  Laura Kovacs, Professor in Computer Science at the TU Wien

  Mar 22, 12:00 - 13:00

  KAUST

  In this talk I will present recent advancement in automated reasoning, in particular computer-supported theorem proving, for generating and proving software properties that prevent programmers from introducing errors while making changes in this software. When testing programs manipulating the computer memory, our initial results show our work is able to prove that over 80% of test cases are guaranteed to have the expected behavior.

Mar 14 - Mar 20, 2021

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  On Discovering Nature’s Equations from Observations

  Jesper Tegner, Professor, BESE Division, KAUST

  Mar 18, 12:00 - 13:00

  KAUST

  In essence, science is about discovering regularities in Nature. It turns out that such regularities (laws) are written in the language of mathematics. In many cases, such laws are formulated and refined from fundamental “first principles.” Yet, in phenomenological areas such as biology, we have an abundance of data but lack “first principles.” Machine learning and deep learning, in particular, are remarkably successful in classification and prediction tasks. However, such systems, when trained on data, do not, as a rule, provide compact mathematical laws or fundamental first principles. Here we ask how we can identify interpretable compact mathematical laws from complex data-sets when we don’t have access to first principles. I will give an overview of this problem and provide some vignettes of our ongoing work in attacking this problem.

Mar 7 - Mar 13, 2021

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  ArtEmis: Affective Language for Visual Art

  Mohamed Elhoseiny, Associate Professor, Computer Science

  Mar 8, 12:00 - 13:00

  KAUST

  We present a novel large-scale dataset and accompanying machine learning models aimed at providing a detailed understanding of the interplay between visual content, its emotional effect, and explanations for the latter in language. In contrast to most existing annotation datasets in computer vision, we focus on the affective experience triggered by visual artworks and ask the annotators to indicate the dominant emotion they feel for a given image and, crucially, to also provide a grounded verbal explanation for their emotion choice.

Feb 28 - Mar 6, 2021

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  Novel computational models in biological imaging

  Xin Gao, Program Chair, Computer Science

  Mar 1, 12:00 - 13:00

  KAUST

  In this talk, I will introduce our recent efforts on developing novel computational models in the field of biological imaging. I will start with the examples in electron tomography, for which I will introduce a robust and efficient scheme for fiducial marker tracking, and then describe a novel constrained reconstruction model towards higher resolution sub-tomogram averaging. I will then show our work on developing deep learning methods for super-resolution fluorescence microscopy.

Feb 21 - Feb 27, 2021

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  Large-Scale Visualization of Scientific Data

  Markus Hadwiger, Professor, Computer Science

  Feb 23, 15:00 - 16:30

  KAUST

  data visualisation

  "A picture is worth a thousand words", and by going beyond static images, interactive visualization has become crucial to exploring, analyzing, and understanding large-scale scientific data. This is true for many areas of science and engineering, such as high-resolution imaging in neuroscience or materials science, as well as in large-scale fluid simulations of the Earth’s atmosphere and oceans, or of trillion-cell oil reservoirs. However, the fact that the amount of data in data-driven sciences is increasing rapidly toward the petascale, and further, presents a tremendous challenge to interactive visualization and analysis. Nowadays, an important enabler of interactivity is often the parallel processing power of GPUs, which, however, requires well-designed customized data structures and algorithms. Furthermore, scientific data sets do not only get larger, they also get more and more complex, and thus have become very hard to interpret and analyze. In this talk, I will give an overview of the research of my group in large-scale scientific visualization, from data structures and algorithms that enable petascale visualization on GPUs, to novel visual abstractions for interactive analysis of highly complex structures in neuroscience, to novel mathematical techniques that leverage differential geometric methods for the detection and visualization of features in large, complex fluid dynamics data on curved surfaces such as the Earth.
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  Look Here! Guiding Attention in Complex Visualizations

  Manuela Waldner, Assistant Professor at the Research Unit of Computer Graphics of the Institute of Visual Computing and Human-Centered Technology at TU Wien, Austria

  Feb 22, 12:00 - 13:00

  KAUST

  Drawing the user's attention to important items in an image, a complex visualization, or a cluttered graphical user interface is a non-trivial challenge. In the context of visualization, our goal is to effectively attract the user's attention to relevant items in large and complex scenes, while keeping noticeable modifications of the image to a minimum. In this talk, I will give an overview of common highlighting methods and present results from my research on attention guidance in complex, dynamic visualizations.

Feb 14 - Feb 20, 2021

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  Nanovisualization: Depicting the Tiny World Around Us

  Ivan Viola, Associate Professor, Computer Science

  Feb 17, 11:30 - 13:00

  KAUST

  nanovisualization

  Life at micron-scale is inaccessible to the naked eye. To aid the comprehension of nano- and micro-scale structural complexity, we utilize 3D visualization. Thanks to efficient GPU-accelerated algorithms, we witness a dramatic boost in the sheer size of structures that can be visually explored. As of today an atomistic model of the entire bacterial cell can be displayed interactively. On top of that, advanced 3D visualizations efficiently convey the multi-scale hierarchical architecture and cope with the high degree of structural occlusion which comes with dense packing of biological building blocks. To further scale up the size of life forms that can be visually explored, the rendering pipeline needs to integrate runtime construction of the biological structure. Assembly rules define how a body of a certain biological entity is composed. Such rules need to be applied on-the-fly, depending on where the viewer is currently located in the 3D scene to generate full structural detail for that part of the scene. We will review how to construct membrane-like structures, soluble protein distributions, and fiber strands through parallel algorithms, resulting in a collision-free biologically-valid scene. Assembly rules that define how a life form is structurally built need to be expressed in an intuitive way for the domain scientist, possibly directly in the three-dimensional space. Instead of placing one biological element next to another one for the entire biological structure by the modelers themselves, only assembly rules need to be specified and the algorithm will complete the application of those rules to form the entire biological entity. These rules are derived from current scientific knowledge and from all available experimental observations. The Cryo-EM Tomography is on rising and shows that we can already now reach the near-atomistic detail when employing smart algorithms. Our assembly rules extraction, therefore, needs to integrate with microscopic observations, to create an atomistic representation of specific, observed life forms, instead of generic models thereof. Such models then can be used in whole-cell simulations, and in the context of automated science dissemination.
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  Resilience is the Cybersecurity of the XXI century, and here's why! - 2021-02-15

  Feb 15, 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.

Feb 7 - Feb 13, 2021

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  Generative Adversarial Networks and Generative Modeling

  Peter Wonka, Professor, Computer Science

  Feb 8, 12:00 - 13:00

  KAUST

  I will review some background of generative adversarial modeling using GANs and discuss how our group has been using GANs for image editing.
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  Leveraging Readily Available Data for Low Resource Natural Language Processing

  Derry Wijaya, Assistant Professor, Computer Science, Boston University

  Feb 7, 15:00 - 16:00

  KAUST

  Natural Language Processing

  State-of-the-art Natural Language Processing (NLP) systems nowadays are dominated by machine learning and deep learning models. However, most of these models often only work well when there are abundant labeled data for training. Furthermore, as these models typically have a large number of parameters, they require large compute resources to train. For the majority of languages in the world and the researchers working on these languages, however, abundant labeled data are a privilege and so do compute resources. How can we train generalizable NLP models that are effective even when labeled data are scarce and compute resources are limited? In this talk, I will present some of our solutions that leverage unsupervised or few-shot learning and readily available multilingual resources or multimodal data to improve machine translation and nuanced text classification such as news framing under these low resource settings.

Jan 31 - Feb 6, 2021

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  Enabling Accurate and Efficient Simulation in Visual Computing - Graduate Seminar - CS

  Dominik L. Michels, Associate Professor, Computer Science

  Feb 1, 12:00 - 13:00

  KAUST

  The overarching goal of Prof. Michels' Computational Sciences Group within KAUST's Visual Computing Center is enabling accurate and efficient simulations for applications in Scientific and Visual Computing.

Jan 24 - Jan 30, 2021

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  Building Better Datacenters - The Quest for Low Latency

  Simon Peter, Assistant professor, Computer Science, University of Texas, Austin

  Jan 25, 18:30 - 19:30

  KAUST

  In this talk, I focus on the adoption of low latency persistent memory modules (PMMs). PMMs upend the long-established model of remote storage for distributed file systems. Instead, by colocating computation with PMM storage we can provide applications with much higher IO performance, sub-second application failover, and strong consistency. To demonstrate this, I present Assise, a new distributed file system, based on a persistent, replicated coherence protocol that manages client-local PMM as a linearizable and crash-recoverable cache between applications and slower (and possibly remote) storage.
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  Building Latency-Critical Datacenter Systems

  Marios Kogias, Researcher, Computer Science, Microsoft Research, Cambridge

  Jan 24, 10:00 - 11:00

  KAUST

  In the first part of the talk, I will focus on ZygOS[SOSP 2017], a system optimized for μs-scale, in-memory computing on multicore servers. ZygOS implements a work-conserving scheduler within a specialized operating system designed for high request rates and a large number of network connections. ZygOS revealed the challenges associated with serving remote procedure calls (RPCs) on top of a byte-stream oriented protocol, such as TCP. In the second part of the talk, I will present R2P2[ATC 2019]. R2P2 is a transport protocol specifically designed for datacenter RPCs, that exposes the RPC abstraction to the endpoints and the network, making RPCs first-class datacenter citizens. R2P2 enables pushing functionality, such as scheduling, fault-tolerance, and tail-tolerance, inside the transport protocol, making it application-agnostic. I will show how using R2P2 allowed us to offload RPC scheduling to programmable switches that can schedule requests directly on individual cores.

Jan 17 - Jan 23, 2021

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  Building Scalable Network Stacks for Modern Applications

  Ahmed Saeed, Postdoctoral Associate, Computer Science, MIT

  Jan 17, 15:00 - 16:00

  KAUST

  This talk covers two research directions that address the shortcomings of existing network stacks. The first is on scalable software network stacks, solving problems in different components of operating systems and applications to allow a single server to handle data flows for tens of thousands of clients. The second is on Wide Area Network (WAN) congestion control, focusing on network-assisted congestion control schemes, where end-to-end solutions fail. The talk will conclude with a discussion of plans for future research in this area.

Nov 29 - Dec 5, 2020

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  Modelling and Simulation of Biological Systems - 2020-12-03

  Gabriel Wittum, Professor, Applied Mathematics and Computational Sciences

  Dec 3, 12:00 - 13:00

  KAUST

  Biological systems are distinguished by their enormous complexity and variability. That is why mathematical modeling and computational simulation of those systems is very difficult, in particular thinking of detailed models which are based on first principles. The difficulties start with geometric modeling which needs to extract basic structures from highly complex and variable phenotypes, on the other hand also has to take the statistic variability into account. Moreover, the models of the processes running on these geometries are not yet well established, since these are equally complex and often couple many scales in space and time. Thus, simulating such systems always means to put the whole frame to test, from modelling to the numerical methods and software tools used for simulation. These need to be advanced in connection with validating simulation results by comparing them to experiments.
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  Enabling Accurate and Efficient Simulation in Visual Computing - Public Colloquium

  Dominik L. Michels, Associate Professor, Computer Science

  Nov 30, 14:30 - 16:00

  KAUST

  visual computing

  The overarching goal of Prof. Michels' Computational Sciences Group within KAUST's Visual Computing Center is enabling accurate and efficient simulations for applications in Scientific and Visual Computing. Towards this goal, the group develops new principled computational methods based on solid theoretical foundations. This talk covers a selection of previous and current work presenting a broad spectrum of research highlights ranging from simulating stiff phenomena such as the dynamics of fibers and textiles, over liquids containing magnetic particles, to the development of complex ecosystems and weather phenomena. Moreover, connection points to the growing field of machine learning are addressed and an outlook is provided with respect to selected technology transfer activities.
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  Research at the Image and Video Understanding Lab (IVUL) - Graduate Seminar - CS

  Bernard Ghanem, Professor, Electrical and Computer Engineering

  Nov 30, 12:00 - 13:00

  KAUST

  In this talk, I will give an overview of research done in the Image and Video Understanding Lab (IVUL) at KAUST. At IVUL, we work on topics that are important to the computer vision (CV) and machine learning (ML) communities, with emphasis on three research themes: Theme 1 (Video Understanding), Theme 2 (Visual Computing for Automated Navigation), Theme 3 (Fundamentals/Foundations).

Nov 22 - Nov 28, 2020

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  Towards Accurate Biomedical Genomics Anywhere Anytime - Public Colloquium

  Xin Gao, Program Chair, Computer Science

  Nov 24, 14:00 - 15:30

  KAUST

  In this talk, I will first give an overview of the research activities in Structural and Functional Bioinformatics Group (http://sfb.kaust.edu.sa). I will then focus on our efforts on developing computational methods to tackle key open problems in Nanopore sequencing. In particular, I will introduce our recent works on developing a collection of computational methods to decode raw electrical current signal sequences into DNA sequences, to simulate raw signals of Nanopore, and to efficiently and accurately align electrical current signal sequences with DNA sequences. I will further introduce their applications in biomedicine and healthcare.
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  From a Virtual Red Sea to the Urban Scales of NEOM: A Decade of Progress Driven by Computational Sciences

  Ibrahim Hoteit, Professor, Earth Science and Engineering

  Nov 23, 12:00 - 13:00

  KAUST

  The talk will present our decade-long efforts to build an integrated data-driven modeling system to study and predict the circulation and climate of the Arabian Peninsula at all scales. Starting from a general description of the Virtual Red Sea Initiative at its achievements so far, I will then outline our ongoing research under the KAUST Centre of Excellence for NEOM to develop new tools to seamlessly project and study the environment at the urban scales of NEOM. I will in particular discuss the involved scientific opportunities and challenges in terms of computational Sciences, including our extreme computational requirements, and the handling, analysis and visualization of very large datasets.

Nov 15 - Nov 21, 2020

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  Start your Research Journey at KAUST - Master and Ph.D. degrees in Applied Mathematics, Computer Science, Electrical Engineering and Statistics

  Aigars Ekers, Assistant Dean, Computer, Electrical and Mathematical Sciences and Engineering

  Nov 18, 16:00 - 17:00

  KAUST

  Did you know that courses are taught in English at KAUST? Did you know that the faculty and students are from more than 100 countries? Did you know that you will be fully funded when studying at KAUST? What you will learn from this webinar: Life at KAUST: What are KAUST and CEMSE? Degrees and Academic Programs Research Areas and Lab Facilities Eligibility and Admission Requirements KAUST Fellowship: Benefits of studying at KAUST How to Apply?
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  Federated Learning of a Mixture of Global and Local Models

  Filip Hanzely, Ph.D., Applied Mathematics and Computational Sciences

  Nov 16, 12:00 - 13:00

  KAUST

  We propose a new optimization formulation for training federated learning models. The standard formulation has the form of an empirical risk minimization problem constructed to find a single global model trained from the private data stored across all participating devices. In contrast, our formulation seeks an explicit trade-off between this traditional global model and the local models, which can be learned by each device from its own private data without any communication. Further, we develop several efficient variants of SGD (with and without partial participation and with and without variance reduction) for solving the new formulation and prove communication complexity guarantees.

Nov 8 - Nov 14, 2020

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  Exploring Entity Relationship in Pairwise Ranking: Adaptive Sampler and Beyond

  Lu Yu, Ph.D., Computer Science

  Nov 11, 16:00 - 18:00

  KAUST

  Living in the booming age of information, we have to rely on powerful information retrieval tools to seek the unique piece of desired knowledge from such a big data world, like using personalized search engines and recommendation systems. In this thesis, we aim at advancing the development of the methodologies and principles of mining heterogeneous information networks through learning entity relations from a pairwise learning to rank optimization perspective. More specifically we first show the connections of different relation learning objectives modified from different ranking metrics including both pairwise and list-wise objectives. We prove that most of popular ranking metrics can be optimized in the same lower bound. Secondly, we propose the class-imbalance problem imposed by entity relation comparison in ranking objectives, and prove that class-imbalance problems can lead to frequency clustering and gradient vanishment problems. As a response, we indicate out that developing a fast adaptive sampling method is very essential to boost the pairwise ranking model. To model the entity dynamic dependency, we propose to unify the individual-level interaction and union-level interactions, and result in a multi-order attentive ranking model to improve the preference inference from multiple views.
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  Interactive Visual Data Analysis

  Eduard Gröller, Associate Professor of Practical Informatics at the Vienna University of Technology

  Nov 9, 12:00 - 13:00

  KAUST

  Visualization and visual computing use computer-supported, interactive, visual representations of (abstract) data to amplify cognition. In recent years data complexity concerning volume, veracity, velocity, and variety has increased considerably. This is due to new data sources as well as the availability of uncertainty, error, and tolerance information.

Nov 1 - Nov 7, 2020

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  Making RPCs first class datacenter citizens

  Marios Kogias, Researcher, Microsoft Research

  Nov 2, 12:00 - 13:00

  KAUST

  I’ll cover thee different RPC policies implemented on top of R2P2. Specifically, we’ll see how R2P2 enables efficient in-network RPC load balancing based on a novel join-bounded-shortest-queue (JBSQ) policy. JBSQ lowers tail latency by centralizing pending RPCs in the middle box and ensures that requests are only routed to servers with a bounded number of outstanding requests. Then, I’ll talk about SVEN, an SLO-aware RPC admission control mechanism implemented as an R2P2 policy on P4 programmable switches. Finally, I’ll describe HovercRaft, a new approach to building fault-tolerant generic RPC services by integrating state-machine replication in the transport layer.

Oct 25 - Oct 31, 2020

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  Compressed communication in Distributed Deep Learning - A Systems perspective

  Panagiotis Kalnis, Professor, Computer Science

  Oct 26, 12:00 - 13:00

  KAUST

  Deep learning

  Network communication is a major bottleneck in large-scale distributed deep learning. To minimize the problem, many compressed communication schemes, in the form of quantization or sparsification, have been proposed. We investigate them from the Computer Systems perspective, under real-life deployments. We identify discrepancies between the theoretical proposals and the actual implementations, and analyze the impact on convergence.

Oct 18 - Oct 24, 2020

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  Automatic Biomedical Hypothesis Generation by Learning from Dynamic Graphs

  Prof. Xiangliang Zhang

  Oct 18, 14:00 - 15:00

  KAUST

  This talk will introduce two novel models we developed for automatic HG generation in two different settings, positive-negative learning and positive-unlabeled learning. We demonstrate the efficacy of the proposed model on three real-world datasets constructed from biomedical publications.