Events

Oct 11 - Oct 17, 2020

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  Dynamic Programming Multi-Objective Combinatorial Optimization

  Michal A. Mankowski, Postdoctoral Research Fellow, Computer Science

  Oct 13, 15:00 - 16:00

  KAUST

  combinatorial optimization global sequence alignment multi-stage optimization algorithm

  In this dissertation, we consider extensions of dynamic programming for combinatorial optimization. We introduce two exact multi-objective optimization algorithms: the multi-stage optimization algorithm that optimizes the problem relative to the ordered sequence of objectives (lexicographic optimization) and the bi-criteria optimization algorithm that simultaneously optimizes the problem relative to two objectives (Pareto optimization).
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  Visualization and segmentation in Geology and Oil exploration

  Daniel Patel, Bergen University College, Norway

  Oct 12, 12:00 - 13:00

  KAUST

  The talk will consist of two parts. First an introduction to important subsurface structures in relation to hydrocarbon extraction are given, followed by an overview of techniques to segment out and visualize these structures. The talk will include topics such as ground truth visualization of measured seismic data, automated object extraction for getting computer assistance in segmenting important structures in the seismic data such as horizons and faults and creation, and visualizing and visual fusion of multiattribute seismic data using the GPU.

Oct 4 - Oct 10, 2020

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  Extensions of Dynamic Programming for Combinatorial Optimization and Data Mining - 2020

  Mikhail Moshkov, Professor, Applied Mathematics and Computational Sciences

  Oct 5, 12:00 - 13:00

  KAUST

  We developed extensions of dynamic programming which allow us (i) to describe the set of objects under consideration, (ii) to perform a multi-stage optimization of objects relative to different criteria, (iii) to count the number of optimal objects, (iv) to find the set of Pareto optimal points for the bi-criteria optimization problem, and (v) to study the relationships between two criteria.

Sep 27 - Oct 3, 2020

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  Personalized, Predictive and Preventive Visual Analytics for Cancer Treatment

  Renata Raidou, Assistant Professor (Tenure Track) in Medical Visualization and Visual Analytics, and a Rosalind Franklin Fellow at the Bernoulli Institute of the University of Groningen, Netherlands

  Sep 28, 12:00 - 13:00

  KAUST

  The term P4 medicine has been coined almost a decade ago to indicate novel ways of early detection and prevention of diseases. P4 stands for personalized, predictive, preventive, and participatory, to indicate that a diagnosis or treatment is tailored to each individual patient, risk factors are identified early and addressed before manifestation, and individuals are actively involved into all processes. Often, P4 approaches are accompanied by the acquisition of large and complex medical imaging data, and demanding computational processes–especially, when it comes to cancer radiotherapy, which is a data heavy and visual computing rich process.

Sep 20 - Sep 26, 2020

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  Data-sparse Algorithms for Large-scale Applications on Emerging Architectures

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

  Sep 21, 12:00 - 13:00

  KAUST

  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. They may be regarded as algebraic generalizations of the fast multipole method.

Sep 13 - Sep 19, 2020

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  Automating Guidance in Nanovisualization - Graduate Seminar

  Ivan Viola, Professor, Computer Science

  Sep 14, 12:00 - 13:00

  KAUST

  Research in visualization and computer graphics has developed techniques to geometrically model objects from our everyday life, or from various branches of industry and science, including modeling life-forms that are of submicron in size. These are not visible to the naked eye and most of us are unfamiliar with structures that form life organized in an assembly of biomolecules. Here visualization techniques can be of tremendous help to guide the viewers to familiarize themselves with what they see and make the engaging visual exploration of these complex structures to an intellectual enrichment.
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  Learning from Scholarly Attributed Graphs for Scientific Discovery

  Uchenna Akujuobi, Ph.D., Computer Science

  Sep 14, 11:30 - 12:30

  KAUST

  Research and experimentation in various scientific fields are based on the knowledge and ideas from scholarly literature. The advancement of research and development has, thus, strengthened the importance of literary analysis and understanding. However, in recent years, researchers have been facing massive scholarly documents published at an exponentially increasing rate. Analyzing this vast number of publications is far beyond the capability of individual researchers. This dissertation is motivated by the need for large scale analyses of the exploding number of scholarly literature for scientific knowledge discovery and information retrieval. In the first part of this dissertation, the interdependencies between scholarly literature are studied. First, I develop Delve -- a data-driven search engine supported by our designed semi-supervised edge classification method. This system enables users to search and analyze the relationship between datasets and scholarly literature. Based on the Delve system, I propose to study information extraction as a node classification problem in attributed networks. Specifically, if we can learn the research topics of documents (nodes in a network), we can aggregate documents by topics and retrieve information specific to each topic (e.g., top-k popular datasets).

Sep 6 - Sep 12, 2020

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  On Studying Computer Science - 2020-09-07

  Peter Wonka, Professor, Computer Science

  Sep 7, 12:00 - 13:00

  KAUST

  Computer science

  The talk will be pre-recorded and a youtube link will be provided to seminar participants. During the seminar time, Prof. Wonka will mainly answer some questions from the audience.

Aug 30 - Sep 5, 2020

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  Machine Learning Models for Biomedical Ontology Integration and Analysis

  Fatima Zohra Smaili, Ph.D. Student, Computer Science

  Sep 3, 16:00 - 17:00

  KAUST

  Biological knowledge is widely represented in the form of ontologies and ontology-based annotations. The structure and information contained in ontologies and their annotations make them valuable for use in machine learning, data analysis and knowledge extraction tasks. In this thesis, we propose the first approaches that can exploit all of the information encoded in ontologies, both formal and informal, to learn feature embeddings of biological concepts and biological entities based on their annotations to ontologies by applying transfer learning on the literature. To optimize learning that combines ontologies and natural language data such as the literature, we also propose a new approach that uses self-normalization with a deep Siamese neural network to improve learning from both the formal knowledge within ontologies and textual data. We validate the proposed algorithms by applying them to generate feature representations of proteins, and of genes and diseases.
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  Large-Scale Visualization of Scientific Data: Volume Data and Flow Fields - 2020-08-31

  Markus Hadwiger, Professor, Computer Science

  Aug 31, 12:00 - 13:00

  KAUST

  ‌ Abstract Welcome to the new semester! 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

Jul 5 - Jul 11, 2020

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  Maximizing I/O Bandwidth for Out-of-Core HPC Applications on Heterogeneous Large-Scale Systems

  Tariq Alturkestani, Ph.D. Student, Computer Science

  Jul 9, 16:00 - 17:00

  KAUST

  Out-of-Core simulation systems often produce a massive amount of data that cannot fit on the aggregate fast memory of the compute nodes, and they also require to read back these data for computation. As a result, I/O data movement can be a bottleneck in large-scale simulations. Advances in memory architecture have made it feasible to integrate hierarchical storage media on large-scale systems, starting from the traditional Parallel File Systems to intermediate fast disk technologies (e.g., node-local and remote-shared NVMe and SSD-based Burst Buffers) and up to CPU’s main memory and GPU’s High Bandwidth Memory. However, while adding additional and faster storage media increases I/O bandwidth, it pressures the CPU, as it becomes responsible for managing and moving data between these layers of storage. Simulation systems are thus vulnerable to being blocked by I/O operations. The Multilayer Buffer System (MLBS) proposed in this research demonstrates a general method for overlapping I/O with computation that helps to ameliorate the strain on the processors through asynchronous access. The main idea consists in decoupling I/O operations from computational phases using dedicated hardware resources to perform expensive context switches. By continually prefetching up and down across all hardware layers of the memory/storage subsystems, MLBS transforms the original I/O-bound behavior of evaluated applications and shifts it closer to a memory-bound or compute-bound regime.

May 3 - May 9, 2020

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  Modeling and Automated Storytelling of Mesoscale Molecular Models

  Ivan Viola, Professor, Computer Science

  May 4, 12:00 - 13:00

  KAUST

  multi-scale 3D visualization biological models

  In my talk, I will present techniques that allow biologists to model a mesocale entity in a rapid way in the timeframe of a few minutes to hours. This way we have created the first complete atomistic model of the SARS-CoV-2 virion that we are these days sharing with the worldwide scientific community. Mesoscale represents a scalar gap that is currently not possible to accurately image with neither microscopy nor X-ray crystallography approaches. For this purpose, scientists characterize it by observations from the surrounding nanoscale and the microscale. From this information, it is possible to reconstruct a three-dimensional model of a biological entity with a full atomistic model. The problem is that these models are enormously large and are not possible to model with traditional methods from computer graphics within a reasonable time.

Apr 26 - May 2, 2020

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  A New Approach to Quad Mesh Processing for Computational Design and Fabrication

  Apr 27, 12:00 - 13:00

  KAUST

  computational design fabrication

  In this talk, we will discuss a new way of computing with quad meshes. It is based on the checkerboard pattern of parallelograms one obtains by subdividing a quad mesh at its edge midpoints. The new approach is easy to understand and implement. It simplifies the transfer from the familiar theory of smooth surfaces to the discrete setting of quad meshes. This is illustrated with applications to constrained editing of 3D models, mesh design for architecture and digital modeling of shapes which can be fabricated by bending flat pieces of inextensible sheet material.

Apr 19 - Apr 25, 2020

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  Simulation of Neuronal Signal Processing - 2020-04-20

  Gabriel Wittum, Professor, Applied Mathematics and Computational Sciences

  Apr 20, 12:00 - 13:00

  KAUST

  Neuronal ensembles

  In the lecture, we present a three-dimensional model for the simulation of signal processing in neurons. Part of this approach is a method to reconstruct the geometric structure of neurons from data measured by 2-photon microscopy. Being able to reconstruct neural geometries and network connectivities from measured data is the basis of understanding coding of motoric perceptions and long term plasticity which is one of the main topics of neuroscience. Other issues are compartment models and upscaling.

Apr 12 - Apr 18, 2020

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  Extensions of Dynamic Programming for Combinatorial Optimization and Data Mining

  Mikhail Moshkov, Professor, Applied Mathematics and Computational Sciences

  Apr 13, 12:00 - 13:00

  KAUST

  data mining Dynamic programming combinatorial optimization

  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. A conventional dynamic programming algorithm returns an optimal object from a given set of objects. We developed extensions of dynamic programming which allow us (i) to describe the set of objects under consideration, (ii) to perform a multi-stage optimization of objects relative to different criteria, (iii) to count the number of optimal objects,(iv) to find the set of Pareto optimal points for the bi-criteria optimization problem, and (v) to study the relationships between two criteria.

Apr 5 - Apr 11, 2020

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  Predicting Single Gene Loss of Function Phenotypes

  Maxat Kulmanov, Research Scientist, Bio-Ontology Research Group

  Apr 6, 19:30 - 21:30

  KAUST

  We developed and expanded novel methods for representation learning, predicting protein functions and their loss of function phenotypes. We use deep neural network algorithm and combine them with symbolic inference into neural-symbolic algorithms. Our work significantly improves previously developed methods for predicting protein functions through methodological advances in machine learning, incorporation of broader data types that may be predictive of functions, and improved systems for neural-symbolic integration. The methods we developed are generic and can be applied to other domains in which similar types of structured and unstructured information exist. In future, our methods can be applied to prediction of protein function for metagenomic samples in order to evaluate the potential for discovery of novel proteins of industrial value. Also our methods can be applied to the prediction of loss of function phenotypes in human genetics and incorporate the results in a variant prioritization tool that can be applied to diagnose patients with Mendelian disorders.
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  Deep Continual Learning

  Mohamed Elhoseiny, Associate Professor, Computer Science

  Apr 6, 12:00 - 13:00

  KAUST

  Deep learning

  In this seminar, I will present some of the work I have done on Continual Deep Learning, among the research topics at the Vision-CAIR group. Continual learning aims to learn new tasks without forgetting previously learned ones. This is especially challenging when one cannot access data from previous tasks and when the model has a fixed capacity as adopted in modern deep learning techniques. Decreasing the gap towards human-level continual learning, we extended continual deep learning from multiple perspectives. The Hebb's learning theory from biology can be famously summarized as “Cells that fire together wire together.". Inspired by this theory from biology, we proposed Memory Aware Synapses (ECCV18) to quantify and reduce machine forgetting in a way that enables leveraging unlabeled data, which was not possible in former techniques. We later developed a Bayesian approach appearing at ICLR2020, where we explicitly modeled uncertainty parameters to orchestrates forgetting in continual learning. We showed in our ICLR2019 and ACCV18 works that task descriptors/ language can operate in continual learning visual tasks to improve learning efficiency and enable zero-shot task transfer. Beyond computer vision tasks, we recently developed an approach appearing at ICLR2020 we call "Compositional Language Continual Learning". We showed that disentangling syntax from semantics enables better compositional Seq2Seq learning and can significantly alleviate forgetting of tasks like machine translation. In the talk, I will go over these techniques and shed some light on future research possibilities.

Mar 29 - Apr 4, 2020

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  Breaking Shackles: Hardware Acceleration Beyond the CPU

  Suhaib Fahmy, Reader, School of Engineering, University of Warwick, UK

  Apr 1, 12:00 - 13:00

  KAUST

  This talk discusses a body of work on exploiting the DSP Blocks in modern FPGAs to construct high-performance datapaths including the concept of FPGA overlays. It outlines work that established FPGAs as a viable virtualized cloud acceleration platform, and how the industry has adopted this model. Finally, it discusses recent work on incorporating accelerated processing in network controllers and the emerging concept of in-network computing with FPGAs. These strands of work come together to demonstrate the value of thinking about computing beyond the CPU-centric view that still dominates.
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  EE+CS Graduate Seminar: Deep Graph Convolutional Networks

  Bernard Ghanem, Professor, Electrical and Computer Engineering

  Mar 29, 12:00 - 13:00

  KAUST

  Deep learning GCN

  In this talk, I will present a line of work done at the Image and Video Understanding Lab (IVUL), which focuses on developing deep graph convolutional networks (DeepGCNs). A GCN is a deep learning network that processes generic graph inputs, thus extending the impact of deep learning to irregular grid data including 3D point clouds and meshes, social graphs, protein interaction graphs, etc. By adapting architectural operations from the CNN realm and reformulating them for graphs, we were the first to show that GCNs can go as deep as CNNs. Developing such a high capacity deep learning platform for generic graphs opens up many opportunities for exciting research, which spans applications in the field of computer vision and beyond, architecture design, and theory. In this talk, I will showcase some of the GCN research done at IVUL and highlight some interesting research questions for future work.

Mar 22 - Mar 28, 2020

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  How to Preserve Privacy in Learning?

  Di Wang, Ph.D. Student, Computer Science and Engineering, State University of New York at Buffalo

  Mar 24, 15:00 - 16:00

  KAUST

  In this talk, I will use the Empirical Risk Minimization (ERM) problem as an example and show how to overcome these challenges. 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.
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  Building Compilers for High Performance Code Generation

  Riyadh Baghdadi, Postdoctoral Associate, Computer Science, MIT

  Mar 22, 15:00 - 16:00

  KAUST

  This talk is about building compilers for high-performance code generation. It has three parts. The first part is about Tiramisu (http://tiramisu-compiler.org/), a polyhedral compiler designed for generating highly efficient code for multicores and GPUs. It is the first polyhedral compiler that can match the performance of highly hand-optimized industrial libraries such as Intel MKL and cuDNN. The second part is about applying Tiramisu to accelerate deep learning (DNN) inference. In comparison to other DNN compilers, Tiramisu has two unique features: (1) it supports sparse DNNs; and (2) it can express and optimize general RNNs (Recurrent Neural Networks). The third part will present recent work on the problem of automatic code optimization. In particular, it will focus on using deep learning to build a cost model to explore the search space of code optimizations.

Mar 15 - Mar 21, 2020

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  On Studying Computer Science - 2020-03-16

  Peter Wonka, Professor, Computer Science

  Mar 16, 12:00 - 13:00

  KAUST

  Computer science

  In this talk, I will discuss a wide range of ideas on studying computer science and doing research in computer science. Peter Wonka is the program chair of the computer science program. His research interests are deep learning, computer graphics, computer vision, and remote sensing.

Mar 1 - Mar 7, 2020

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  Knowledge-based methods in life sciences

  Robert Hoehndorf, Associate Professor, Computer Science

  Mar 3, 10:00 - 11:30

  B9 L2 H2 R2325

  Computer science bioinformatics knowledge representation

  In my research I aim to understand how formalized knowledge bases can be used to systematically structure and integrate biological knowledge, and how to utilize these formalized knowledge bases as background knowledge to improve scientific discovery in biology and biomedicine. To achieve these aims, I develop methods for representing, integrating, and analyzing data and knowledge with the specific aim to make the combination of data and formalized knowledge accessible to data analytics and machine learning in bioinformatics. Biomedicine, and life sciences in general, are an ideal domain for knowledge-driven data analysis methods due to the large number of formal knowledge bases that have been developed to capture the broad, diverse, and heterogeneous data and knowledge.
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  Data-sparse Methods for Large-scale Applications on Emerging Architectures

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

  Mar 2, 12:00 - 13:00

  B9 L2 H1 R2322

  hierarchical computations

  A traditional goal of algorithmic optimality, squeezing out operations, has been superseded because of evolution in architecture. Arithmetic operations no longer serve as a reasonable proxy for all aspects of complexity. Instead, algorithms must now squeeze memory, data transfers, and synchronizations, while extra operations on locally cached data represent only small costs in time and 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 applications for which exascale computers are being constructed. We describe modules of a KAUST-built software toolkit, Hierarchical Computations on Manycore Architectures (HiCMA), that illustrate these features and are building blocks of KAUST mission applications, such as matrix-free higher-order methods in optimization and large-scale spatial statistics. Early modules of this open-source project have undergone industrial-rigor testing are distributed in the software libraries of major vendors.

Feb 23 - Feb 29, 2020

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  Multi-layer Red Team vs. Blue Team Methods towards Secure Cyber-Physical Energy Systems

  Charalambos (Harrys) Konstantinou, Assistant Professor of Electrical and Computer Engineering with Florida A&M University and Florida State University (FAMU-FSU) College of Engineering

  Feb 24, 12:00 - 13:00

  B9 L2 H1

  cybersecurity Cyber attacks and defenses

  Election hacking, power grid cyber-attacks, troll farms, fake news, ransomware, and other terms have entered our daily vocabularies and are here to stay. Cybersecurity touches nearly every part of our daily lives. Most importantly, national security and economic vitality rely on a safe, resilient, and stable cyber-space. We rely on cyber-physical systems with hardware devices, software platforms, and network systems to connect, travel, communicate, power our homes, provide health care, run our economy, etc. However, cyber-threats and attacks have grown exponentially over the past years, exposing both corporate and personal data, disrupting critical operations, causing a public health and safety impact, and imposing high costs on the economy. In this talk, we will focus on cyber-physical energy systems (CPES) as the backbone of critical infrastructure, and provide a research perspective and present red team security threats, challenges, and blue team countermeasures. We will discuss recent approaches on developing low-budget targeted cyberattacks against CPES, designing resilient methods against false data, and the need for an accurate assessment environment achieved through the inclusion of hardware-in-the-loop testbeds.

Feb 16 - Feb 22, 2020

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  Machine Learning Scan of Complete Genome Sequences of 4,500 Rice Varieties reveals Secrets in the Domestication Process of Crops

  Takashi Gojobori, Distinguished Professor, Bioscience

  Feb 19, 12:00 - 13:00

  B9 L2 H2 R2325

  artificial intelligence machine learning

  In the history of humankind, domestication was invented as anthropogenic evolution that fulfills mankind’s critical food demand. The domestication is simply based on mating processes and subsequent selection processes to pick up better hybrid offspring that have advantageous combinations of genomes. Taking advantage of machine learning classifier, we discovered a number of sub-genomic regions that have been incorporated in the rice genomes through production of hybrid offspring during domestication. This so-called “introgression” event is disclosed as an essential key of domestication process. This eventually leads to construction of the AI-aided Smart Breeding Platform to accumulate all the breeding histories of crop species into an Integrated Breeding Knowledgebase.
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  Towards Accurate Biomedical Genomics Anywhere Anytime - Graduate Seminar

  Xin Gao, Program Chair, Computer Science

  Feb 17, 12:00 - 13:00

  B9 L2 H1 R2322

  biomedicine DNA sequencing

  Abstract Current genetic diagnosis by next-generation sequencing requires a large investment of resources and offers little point-of-care portability. Furthermore, it is unable to detect many types of genetic variations - including large deletions, duplications, and balanced translocations - that are relevant to human diseases and health. Comparing to other sequencing technologies, Nanopore sequencing owns the advantages of point-of-care (i.e., sequencing anywhere anytime), long reads (i.e., assembly-free to detect various genetic variations), and PCR free (i.e., sample preparation is easy)
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  Novel computational methods for promoter identification and analysis

  Ramzan Umarov, Ph.D. Student, Computer Science

  Feb 16, 16:00 - 18:00

  B2 L5 R5209

  machine learning promoters protein coding RNA genes TSS prediction tools

  In this dissertation, I present the methods I have developed for prediction of promoters for different organisms. Instead of focusing on the classification accuracy of the discrimination between promoter and non-promoter sequences, I predict the exact positions of the TSS inside the genomic sequences, testing every possible location. The developed methods significantly outperform the previous promoter prediction programs by considerably reducing the number of false positive predictions. Specifically, to reduce the false positive rate, the models are adaptively and iteratively trained by changing the distribution of samples in the training set based on the false positive errors made in the previous iteration. The new methods are used to gain insights into the design principles of the core promoters. Using model analysis, I have identified the most important core promoter elements and their effect on the promoter activity. I have developed a novel general approach to detect long range interactions in the input of a deep learning model, which was used to find related positions inside the promoter region. The final model was applied to the genomes of different species without a significant drop in the performance, demonstrating a high generality of the developed method.

Feb 9 - Feb 15, 2020

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  Optimal Reference Frames, Scaling and Features in Visualization

  Prof. Holger Theisel, Visual Computing, Magdeburg University

  Feb 10, 12:00 - 13:00

  B9 L2 H1 R2322

  visual computing visualization

  In Visualization, the success or failure of an analysis often depends on the choice of some subtle parameters or design choices. While simple heuristics are often sufficient, in some cases they make the analysis miserably fail. We present three approaches in visualization where a careful choice of optimal parameters results in completely new algorithms: 1) the choice of a reference frame for finding objective vortices in flow visualization, 2) the choice of a scaling of high-dimensional data sets for finding linear projections to 2D in information visualization, and 3) the choice of a feature definition along with numerical extraction methods for visualizing recirculation phenomena in flows.

Feb 2 - Feb 8, 2020

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  Optimization and Learning in Computational Imaging

  Wolfgang Heidrich, Professor, Computer Science

  Feb 5, 12:00 - 13:00

  B9 H1 R2322

  computational imaging

  The Machine Learning Hub Seminar Series presents “Optimization and Learning in Computational Imaging” by Dr. Wolfgang Heidrich, Professor in Computer Science at KAUST. He leads the AI Initiative and is the Director of the KAUST Visual Computing Center. Computational imaging systems are based on the joint design of optics and associated image reconstruction algorithms. Historically, many such systems have employed simple transform-based reconstruction methods. Modern optimization methods and priors can drastically improve the reconstruction quality in computational imaging systems. Furthermore, learning-based methods can be used to design the optics along with the reconstruction method, yielding truly end-to-end learned imaging systems, blurring the boundary between imaging hardware and software.
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  Paving the Way for Next Generation Wireless Networks

  Basem Shihada, Professor, Computer Science

  Feb 3, 12:00 - 13:00

  B9 L2 H1 R2322

  networking IoT

  In this talk, I will introduce several ongoing projects at the networking lab in KAUST. I will start by highlighting our research profile along with our mission. Then, I will walk you through our projects and contributions in the domains of the internet of things, visible light communication, underwater communication, and future 6G networks. I will focus on the challenges facing each project, highlight our solution methodology, and discuss some performance evaluation results. I will focus on our work on Aqua-Fi, which aims at bringing the Internet into the underwater environment. I will also focus on our recent project on the communication via breath.
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  Architectural Defenses against Indirect Threats

  Yasser Shalabi, Graduate Student, Computer Science, University of Illinois Urbana-Champaign

  Feb 2, 12:00 - 13:00

  B9 L2 H2

  Computer architecture

  The potential of indirect execution attacks – e.g. Return-Oriented-Programming and Transient-Execution based Side-channels – threaten all modern computing platforms. Standard security policies are unable to eliminate these threats. What is the role of hardware in mitigating these threats? Why are the latest processor designs no longer proactively eliminating threats? In this talk, we will explore these questions and reconsider the role of hardware in securing systems. I will present Record-and-Replay as a fundamental solution that can enable a hardware-software co-design that can strengthen the security of modern computing platforms.

Jan 26 - Feb 1, 2020

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  Account Sharing and Protection

  Prof. Zhongmin Cai, Automation Department, Xi’an Jiaotong University, China

  Jan 29, 12:00 - 13:00

  B1 L4 R4214

  account sharing account protection Xi'an Jiaotong University

  Abstract Our lives and society are digitized from every perspective by computers, smart devices and various information systems. A large amount of accounts are appearing and becoming essential tools to access resources and services in this digital age. Besides the power and convenience, people are also experiencing new challenges and difficulties in their use and protection of various accounts. The first part of this talk will present a recent study on account sharing practices in the workplace. Across two surveys, we will examine the sharing process at work, probing what accounts people share
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  Space-Time Tomographic Reconstruction of Deforming Objects

  Guangming Zang, Research Engineer, Computer Science

  Jan 27, 17:00 - 18:30

  B1 L2 R2202

  inverse problems computational imaging Deep learning optimization computed tomography space-time tomography

  In this thesis, a variety of applications in computer vision and graphics of inverse problems using tomographic imaging modalities will be presented: (i) The first application focuses on the CT reconstruction with a specific emphasis on recovering thin 1D and 2D manifolds embedded in 3D volumes. (ii) The second application is about space-time tomography (iii) Base on the second application, the third one is aiming to improve the tomographic reconstruction of time-varying geometries undergoing faster, non-periodic deformations, by a warp-and-project strategy. Finally, with a physically plausible divergence-free prior for motion estimation, as well as a novel view synthesis technique, we present applications to dynamic fluid imaging which further demonstrates the flexibility of our optimization frameworks
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  Seeing is Believing? Media Integrity in a Post-Truth World

  Prof. Nasir Memon, Vice Dean for Academics and Student Affairs and Professor of Computer Science and Engineering at the New York University Tandon School of Engineering

  Jan 27, 12:00 - 13:00

  B9 L2 H1

  media integrity machine learning deepfake digital media

  The emergence of “fake news” along with sophisticated techniques using machine learning to create realistic looking media such as deepfakes, has led to a renewed interest in digital media forensics. In this talk, Professor Nasir Memon will broadly discuss how media is generated and manipulations have been traditionally detected. He will then look at new approaches using machine learning for creating media that are leading us to a world where images and video cannot be believed any more as they can evade traditional detection techniques. Professor Memon will end by discussing approaches that are being developed to return integrity and trust in digital media.
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  Amazon Web Services (AWS) ML Immersion Day

  Mohib Khan, Hesham Abouelmagd, Shijaz Abdulla (AWS)

  Jan 27, 08:30 - 16:15

  B19 H1

  machine learning artificial intelligence

  The ML Hub with the support of the AI Initiative, is excited to be hosting the AWS ML Immersion Day! Join us for a full-day immersion tutorial and hands-on lab on Amazon’s ML tools. The program includes an introduction to AWS AI and machine learning services and hands-on module on Amazon Lex and SageMaker.
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  Dictionary Attacks on Biometrics

  Prof. Nasir Memon, Vice Dean for Academics and Student Affairs and Professor of Computer Science and Engineering at the New York University Tandon School of Engineering

  Jan 26, 16:00 - 17:00

  B9 L2 H2

  biometrics dictionary attacks user authentication payment authorization

  Contrary to the prevailing belief, we show that user authentication based on biometrics is vulnerable to dictionary attacks. We show the problem is particularly significant for partial prints used in smartphones and increasingly adopted for authentication tasks ranging from unlocking the devices screen up to payment authorization. We also show that speaker verification systems are also vulnerable to dictionary attacks. We then discuss ways to mitigate such attacks.

Jan 19 - Jan 25, 2020

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  KAUST Research Conference Digital Health 2020

  Takashi Gojobori, Ibn Sina Distinguished Professor (former), Bioscience

  Jan 20, 08:00 - Jan 22, 17:00

  B19 L2 H1

  digital health artificial intelligence computational science big data analytics Omics

  Computational Bioscience Research Center at King Abdullah University of Science and Technology is pleased to announce the KAUST Research Conference on Digital Health 2020.

Jan 12 - Jan 18, 2020

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  BBDS: A Privacy-preserving Blockchain-Based Data Sharing System

  Prof. Jin Li, Computer Science, Guangzhou University

  Jan 15, 12:00 - 13:00

  B1 L4 R4214

  internet of things security

  Abstract Nowadays, the devices in the Internet of Things have been widely used. These devices generate a huge amount of sensitive data, which are susceptible to cyber attacks. In this talk, we introduce an efficient blockchain based approach with several computational models of physically unclonable functions (denoted by PUF model). The presented protocol aims to guarantee the authentication of the devices with fast verification process. Furthermore, the combination of Blockchain and PUF motivates us to propose an efficient protocol that guarantees data provenance and data integrity in IoT

Jan 5 - Jan 11, 2020

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  PREMA: Principled Tensor Data Recovery from Multiple Aggregated Views

  Faisal M. Almutairi, Ph.D. Candidate, Electrical and Computer Engineering, University of Minnesota

  Jan 8, 12:00 - 13:00

  B1 L4 R4214

  data disaggregation algorithms University of Minnesota

  The proposed method, called PREMA, leverages low-rank tensor factorization tools to provide recovery guarantees under certain conditions. PREMA is flexible in the sense that it can perform the disaggregation task on data that have missing entries, i.e., partially observed. The proposed method considers challenging scenarios: i) the available views of the data are aggregated in two dimensions, i.e., double aggregation, and ii) the aggregation patterns are unknown. Experiments on real data from different domains, i.e., sales data from retail companies, crime counts, and weather observations, are presented to showcase the effectiveness of PREMA.

Dec 29 - Jan 4, 2019

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  Practical Cryptographic Framework to Manage Security and Privacy for IoT Endpoint devices

  Prof. Chunhua Su, Computer Science, University of Aizu

  Jan 1, 12:00 - 13:00

  B1 L4 R4214

  IoT cybersecurity Cryptography machine learning user authentication user's bio-features device capabilities

  In this talk, the speaker will provide a high-level introduction to his recent research on IoT endpoint security. Firstly, he will introduce requirements followed by a discussion on cryptographic algorithm implementation. He will mainly focus on an overview of efficient cryptography for IoT endpoints and system privacy issues. Then he will discuss security management approaches, positives, negatives and challenges to resolve, linking to the endpoint device security section with regards to realistic device needs/capabilities.

Dec 8 - Dec 14, 2019

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  SLATE: Design of a Modern Distributed and Accelerated Dense Linear Algebra Library

  Dalal Sukkari, Ph.D., Applied Mathematics and Computational Sciences

  Dec 11, 16:00 - 17:00

  B2 L5 R5220

  The SLATE (Software for Linear Algebra Targeting Exascale) library is being developed to provide fundamental dense linear algebra capabilities for current and upcoming distributed high-performance systems, both accelerated CPU–GPU based and CPU based.

Dec 1 - Dec 7, 2019

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  Proximal Splitting Methods for Convex Optimization: An Introduction

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

  Dec 2, 12:00 - 13:00

  B9 L2 H1 R2322

  This talk will be a gentle introduction to proximal splitting algorithms to minimize a sum of possibly nonsmooth convex functions. Several such algorithms date back to the 60s, but the last 10 years have seen the development of new primal-dual splitting algorithms, motivated by the need to solve large-scale problems in signal and image processing, machine learning, and more generally data science. No background will be necessary to attend the talk, whose goal is to present the intuitions behind this class of methods.
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  Enabling smart and connected systems

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

  Dec 1, 12:00 - 13:00

  B9 L2 H1 R2322

  The talk will discuss how recent advances in wireless computing and communication nodes can be harnessed to serve the multitude of deployment scenarios required to empower communities of the future with smart and connected systems. In this talk, we address fundamental questions that should be asked when contemplating future smart and connected systems, namely, How, Where and What? (1) How can we design computing and communication nodes that best utilize resources in a way that is cognizant of both the abilities of the platform, as well as the requirements of the network? (2) Where are the nodes deployed? By understanding the context of deployment, one can architect unique solutions that are currently unimaginable. With the transformation to diverse applications such as body area networking, critical infrastructure monitoring, precision agriculture, autonomous driving, etc., the need for innovative solutions becomes even more amplified. (3) What benefit can be inferred from the data gathered by nodes in the capacity of computing, communication, and sensing?

Nov 24 - Nov 30, 2019

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  Hidden Backdoors in Deep Learning Systems

  Prof. Ben Zhao, Computer Science, University of Chicago, USA

  Nov 25, 12:00 - 13:00

  B9 L2 H1 R2322

  In this talk, I will describe two recent results on detecting and understanding backdoor attacks on deep learning systems. I will first present Neural Cleanse (IEEE S&P 2019), the first robust tool to detect a wide range of backdoors in deep learning models. We use the idea of perturbation distances between classification labels to detect when a backdoor trigger has created shortcuts to misclassification to a particular label. Second, I will also summarize our new work on Latent Backdoors (CCS 2019), a stronger type of backdoor attack that is more difficult to detect and survives retraining in commonly used transfer learning systems. Latent backdoors are robust and stealthy, even against the latest detection tools (including neural cleanse).
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  Network Function Virtualization: What, Why and How?

  Ahmed E. Kamal, Department of Electrical and Computer Engineering, Iowa State University, Ames, IA, USA.

  Nov 24, 11:00 - 12:00

  B1 L3 R3119

  Virtualized Network Function

  The European Telecom Standards Institute (ETSI) introduced the concept of Network Function Virtualization (NFV) with the aim of efficient network architecture and network system operation. In traditional networks, network functions are implemented in dedicated physical machines which are designed for single functionalities. Network services have been provided by connecting these physical machines, so the network architecture has been highly rigid and hard to change. NFV environment provides a more flexible and scalable network configuration and implementation through the softwarization of physical network functions. Network functions are transformed to Virtual Machines (VMs) so that Virtualized Network Functions (VNFs) can be implemented in commodity servers built for common uses, including public clouds.

Nov 17 - Nov 23, 2019

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  Eliminating Waiting Time Inequities for Deceased Donor Kidney Transplantation in the USA

  Dr. Joris van de Klundert, Professor of Operations Management, Prince Mohammad Bin Salman College (MBSC) of Business & Entrepreneurship

  Nov 18, 12:00 - 13:00

  B9 L2 H1 R2322

  organ transplantation wait time modeling Equity Maximization network flows queuing theory Rawls theory

  This research formally models, analyzes and maximizes equity of transplant waiting times and probabilities using queuing theory and network flows, based on Rawls' theory of justice. The presented formal models address inequities resulting from blood type incompatibilities, which are interrelated to ethnic differences in patient and donor rates.

Nov 10 - Nov 16, 2019

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  Langevin Monte Carlo as an optimization algorithm

  Adil Salim, Postdoctoral Research Fellow, Computer Science

  Nov 11, 12:00 - 13:00

  B9 L2 H1 R2322

  machine learning Langevin Monte Carlo optimization

  Adil Salim is mainly interested in stochastic approximation, optimization, and machine learning. He is currently a Postdoctoral Research Fellow working with Professor Peter Richtarik at the Visual Computing Center (VCC) at King Abdullah University of Science and Technology (KAUST).

Nov 3 - Nov 9, 2019

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  Reproducibility in Computer Science - Part 3

  Roy Maxion, Research Professor, Computer Science Department, Carnegie Mellon University

  Nov 6, 16:00 - 17:00

  B9 L3 R3223

  reproducibility issues reliability testing Computer science complex models artificial intelligence machine learning scientific enterprise

  Roy Maxion will give three lectures focusing broadly on different aspects of an increasingly important topic: reproducibility. Reproducibility tests the reliability of an experimental result and is one of the foundations of the entire scientific enterprise. We often hear that certain foods are good for you, and a few years later we learn that they're not. A series of results in cancer research was examined to see if they were reproducible. A startling number of them - 47 out of 53 - were not. Matters of reproducibility are now cropping up in computer science, and given the importance of computing in the world, it's essential that our own results are reproducible -- perhaps especially the ones based on complex models or data sets, and artificial intelligence or machine learning. This lecture series will expose attendees to several issues in ensuring reproducibility, with the goal of teaching students (and others) some of the crucial aspects of making their own science reproducible. Hint: it goes much farther than merely making your data available to the public.
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  Exploranation: A New Science Communication Paradigm

  Prof. Anders Ynnerman, Linköping University, Sweden

  Nov 6, 09:00 - 10:00

  B9 H2 R2325

  Abstract Science communication is facing a paradigm shift, based on the convergence of exploratory and explanatory visualization. In this article, we coin the term exploranation to denote the way in which visualization methods from scientific exploration can be used to communicate results and how methods in explanatory visualization can enrich exploration. Brief Biography Professor Ynnerman is currently the head of the Media and Information Technology division at the Department of Science and Technology (ITN) at Linköping University and is the director of the Norrköping Visualization Center C