Events

May 2 - Apr 26, 2020

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  Semi-Talk: Multifunctional Flexible Semiconductor Materials and Devices for Photonic, Electronic, Energy, and Sensing Applications

  Prof. Jae-Hyun Ryou, Mechanical Engineering, Texas Center for Superconducitity

  May 1, 16:00 - 17:00

  KAUST

  Brief Biography Prof. Jae-Hyun Ryou is an Associate Professor of Mechanical Engineering Also affiliate with Materials Science and Engineering Program, Texas Center for Superconducitity (TcSUH), and Advanced Manufacturing Institute (AMI) Education and experience: Ph.D. in Materials Science and Engineering, University of Texas at Austin M.S. and B.S. in Metallurgical Engineering, Yonsei University, Seoul, Korea Honeywell Technology Center Center for Compound Semiconductors at Georgia Institute of Technology Research: Current research interests: semiconductor materials, devices, and
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  On Optimal Regularization in Estimation, Detection, and Classification

  Tareq Al-Naffouri, Professor, Electrical and Computer Engineering

  Apr 30, 12:00 - 13:00

  KAUST

  linear systems computational Complexity Convex Gaussian Min-max theorem

  In many problems in statistical signal processing, regularization is employed to deal with uncertainty, ill-posedness, and insufficiency of training data. It is possible to tune these regularizers optimally asymptotically, i.e. when the dimension of the problem becomes very large, by using tools from random matrix theory and Gauss Process Theory. In this talk, we demonstrate the optimal turning of regularization for three problems : i) Regularized least squares for solving ill-posed and/or uncertain linear systems, 2) Regularized least squares for signal detection in multiple antenna communication systems and 3) Regularized linear and quadratic discriminant binary classifiers.
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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.
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  Distributed Robotics

  Apr 26, 12:00 - 13:00

  KAUST

  robotics distributed systems

  This talk presents an overview of challenges, state-of the-art, and applications for distributed robotic systems. In distributed robotic systems, there is a group of robots that seek to achieve a collective task. Applications include environmental monitoring, search and rescue, and programmable self-assembly. Settings can range from a small team of cooperative robots to a swarm of many interacting agents. An essential feature of such systems is that individual robots make decisions based on available local information as well as limited communications with other robots. The challenge is to design local protocols that result in desired global outcomes. In contrast to a traditional centralized paradigm, both measurements and decisions are distributed among multiple actors.

Apr 25 - Apr 19, 2020

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  Efficient and Physics-Preserving Discretization Schemes for Multiphase Flow Problems

  Apr 23, 12:00 - 13:00

  KAUST

  No summary is available.
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  Nanomaterials and Nanostructure Based Energy Efficient In-Memory Sensing

  Nazek El-Atab, Assistant Professor, Electrical and Computer Engineering

  Apr 20, 12:00 - 13:00

  KAUST

  nanomaterials Nanostructure energy efficiency

  In this talk, I will present the potential of nano-islands grown by the atomic layer deposition (ALD) in addition to novel material combinations in scaled memory devices. Moreover, in order to relax the scalability requirement, I will introduce an integrated potential single device solution by infusing memory device which can monitor and remember our actions and the surrounding environment. Such a device performs the roles of both a MEMory and a senSOR at once and is referred to as a MEMSOR. The MEMSOR is directly programmed by external physical stimuli rather than an applied electric potential as in conventional devices. As a result, the MEMSOR leads to faster data analysis, lower footprint area, energy consumption, and ideally, the cost of the system. Finally, the MEMSOR device will be based on a MOSFET architecture with a mature manufacturing process, and thus it can be integrated with conventional Flash devices on the same silicon wafer with minimal additional process steps.
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  Simulation of Neuronal Signal Processing - 2020-04-20

  Gabriel Wittum, Professor (former), Applied Mathematics and Computational Science

  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.
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  Near Field Wireless Power Transfer: Circuits and Application

  Shehab Ahmed, Professor, Electrical and Computer Engineering

  Apr 19, 12:00 - 13:00

  KAUST

  Near Field wireless Power Transfer

  Recently, wireless power transfer has been used in mobile phones, electric vehicles, medical implants, wireless sensor networks, unmanned aerial vehicles, and so on. In this seminar, we will walk through circuit theory and simulations that illustrate the nature and constraints of the technology. We will also address contemporary topics related to application of wireless power transfer in electric vehicle applications.

Apr 18 - Apr 12, 2020

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  Economic Model Predictive Control Strategies for Nonlinear Systems

  Fahad Albalawi , Research Scientist, Electrical and Computer Engineering

  Apr 16, 16:00 - 17:30

  KAUST

  Model Predictive Control neural network nonlinear control systems

  The first part of this talk provides a brief introduction of the EMPC (motivation, challenges, and solutions). The second part of this talk proposes a regret-based robust EMPC paradigm for nonlinear systems subject to unknown but bounded disturbance. The main motivation of the proposed work is the possible improvement of the economic performance when one considers the regret function as the objective function for the robust EMPC algorithm instead of the worst cost. The third part of this talk introduces an integrated framework that combines a Neural Network (NN) algorithm with an MPC scheme that can guarantee closed-loop stability in the presence of deception cyberattacks (e.g., min-max cyberattack). Both discrete-time and continuous-time nonlinear systems will be utilized throughout the talk to demonstrate the applicability and effectiveness of the proposed control methods. Finally, future research directions will be presented at the end of the talk.
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  Quantitative understanding of transcription factor regulation at network- and molecular-levels through optimization and deep learning

  Xin Gao, Professor, Computer Science

  Apr 16, 12:00 - 13:00

  KAUST

  transcription factor optimization Deep learning

  Transcription factors are an important family of proteins that control the transcription rate from DNAs to messenger RNAs through the binding to specific DNA sequences. Transcription factor regulation is thus fundamental to understanding not only the system-level behaviors of gene regulatory networks, but also the molecular mechanisms underpinning endogenous gene regulation. In this talk, I will introduce our efforts on developing novel optimization and deep learning methods to quantitatively understanding transcription factor regulation at network- and molecular-levels. Specifically, I will talk about how we estimate the kinetic parameters from sparse time-series readout of gene circuit models, and how we model the relationship between the transcription factor binding sites and their binding affinities.
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  Extensions of Dynamic Programming for Combinatorial Optimization and Data Mining

  Mikhail Moshkov, Professor, Applied Mathematics and Computational Science

  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.
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  Evolutionary science via nonlinear orthography

  Andrea Fratalocchi, Professor, Electrical and Computer Engineering

  Apr 12, 12:00 - 13:00

  KAUST

  Evolutionary science nonlinear orthography

  The goal of our research is to understand the physical origin of these behaviors and trasform them into sustainable technologies that tackle contemporary problem of global interest, ranging from energy harvesting to clean water production, design of smart materials, biomedical applications, information security, artificial intelligence, global warming, and so on. Creating technologies from complex natural systems is a modern interdisciplinary research field that permeates many different scientific areas, ranging from physics to mathematics, to engineering and the theory of linguistics. This is a very challenging, yet very promising research. It involves the understanding of what we consider complex, which translates as something “involved, intricate, complicated, not easily understood or analyzed”. This sets the challenge of being able to understand the mechanisms of these systems and cross many different disciplines in order to constructively harness their properties into reproducible applications.

Apr 11 - Apr 5, 2020

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  Geometry and Computational Design

  Apr 9, 12:00 - 13:00

  KAUST

  Geometry computational design

  An important stream of research in computational design aims at digital tools which support users in realizing their design intent in a simple and intuitive way, while simultaneously taking care of key aspects of function and fabrication. Such tools are expected to shorten the product development cycle through a reduction of costly feedback loops between design, engineering and fabrication. The strong coupling between shape generation, function and fabrication is a rich source for the development of new geometric concepts, with an impact to the original applications as well as to geometric theory. This will be illustrated at hand of applications in architecture and fabrication with a mathematical focus on discrete differential geometry and geometric optimization problems.
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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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  Computation of High-Dimensional Multivariate Normal and Student-t Probabilities Based on Matrix Compression Schemes

  Jian Cao, Ph.D., Statistics

  Apr 6, 16:00 - 18:00

  KAUST

  The thesis focuses on the computation of high-dimensional multivariate normal (MVN) and multivariate Student-t (MVT) probabilities. Firstly, a generalization of the conditioning method for MVN probabilities is proposed and combined with the hierarchical matrix representation. Next, I revisit the Quasi-Monte Carlo (QMC) method and improve the state-of-the-art QMC method for MVN probabilities with block reordering, resulting in a ten-time-speed improvement. The thesis proceeds to discuss a novel matrix compression scheme using Kronecker products. This novel matrix compression method has a memory footprint smaller than the hierarchical matrices by more than one order of magnitude. A Cholesky factorization algorithm is correspondingly designed and shown to accomplish the factorization in 1 million dimensions within 600 seconds. To make the computational methods for MVN probabilities more accessible, I introduce an R package that implements the methods developed in this thesis and show that the package is currently the most scalable package for computing MVN probabilities in R. Finally, as an application, I derive the posterior properties of the probit Gaussian random field and show that the R package I introduce makes the model selection and posterior prediction feasible in high dimensions.
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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.
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  Blue LED material extended to Green, Yellow, and now Red

  Kazuhiro Ohkawa, Professor (former), Electrical and Computer Engineering

  Apr 5, 12:00 - 13:00

  KAUST

  electrical engineering energy LED OLEDs MOCVD

  In this seminar, the science of MOCVD, the material science of InGaN, and the new-born InGaN-based red LED performance will be discussed. The three primary colors in light are RGB. Green and blue LEDs have been realized by using InGaN active region. The current red LEDs are based on AlGaAs or InGaP as the active region. If we can realize red LEDs by InGaN, it is possible to integrate RGB LEDs in a wafer. Such RGB integration is a breakthrough to develop the next displays, so-called, micro-LED displays that are the next after the OLED displays, and functional LED lightings.

Apr 4 - Mar 29, 2020

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  Fabrication and Characterization of GaN-Based Superluminescent Diode for Solid-State Lighting and Visible Light Communication

  Abdullah A. Alatawi, Ph.D. Student, Electrical and Computer Engineering

  Apr 2, 16:00 - 18:00

  KAUST

  Fabrication and Characterization solid-state lighting Visible light communications

  This dissertation is devoted to the fabrication and electrical and optical characterization of a new class of III-nitride light-emitter known as superluminescent diode (SLD). SLD works in an amplified spontaneous emission (ASE) regime, and it combines several advantages from both LD and LED, such as droop-free, speckle-free, low-spatial coherence, broader emission, high-optical power, and directional beam. Here, SLDs were fabricated by a focused ion beam by tilting the front facet of the waveguide to suppress the lasing mode. They showed a high-power of 474 mW on c-plane GaN-substrate with a large spectral bandwidth of 6.5 nm at an optical power of 105 mW. To generate SLD-based white light, a YAG-phosphor-plate was integrated, and a CRI of 85.1 and CCT of 3392 K were measured. For the VLC link, SLD showed record high-data rates of 1.45 Gbps and 3.4 Gbps by OOK and DMT modulation schemes, respectively. Additionally, a widely single- and dual-wavelength tunability were designed using SLD-based external cavity (SLD-EC) configuration for a tunable blue laser source.
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  Classification of Functional Data by Detecting the Discrepancy of Second Moment Structure of Scaled functions

  Shuhao Jiao, Postdoctoral Research Fellow, Statistics

  Apr 2, 12:00 - 13:00

  KAUST

  Functional data

  This talk presents a new classification method for functional data. We consider the case where different groups of functions have similar means so that it is difficult to classify them based on only the mean function. To overcome this limitation, we propose the second moment based functional classifier (SMFC). Here, we demonstrate that the new method is sensitive to divergence in the second moment structure and thus produces lower rate of misclassification compared to other competitor methods. Our method uses the Hilbert-Schmidt norm to measure the divergence of second moment structure. One important innovation of our classification procedure lies in the dimension reduction step. The method data-adaptively discovers the basis functions that best capture the discrepancy between the second moment structures of the groups, rather than uses the functional principal component of each individual group, and good performance can be achieved as unnecessary variability is removed so that the classification accuracy is improved. Consistency properties of the classification procedure and the relevant estimators are established. Simulation study and real data analysis on phoneme and rat brain activity trajectories empirically validate the superiority of the proposed method.
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  Efficient and Accurate Numerical Techniques for Sparse Electromagnetic Imaging

  Ali Imran Sandhu, Ph.D. Student, Electrical and Computer Engineering

  Apr 1, 15:30 - 17:30

  KAUST

  computational electromagnetics inverse scattering

  In this thesis, efficient solutions are sought out to fundamental problems in Electromagnetic (EM) imaging that determines the shape, location, and material properties of an (unknown) object of interest in an investigation domain from the scattered field measured away from it. The solution of an EM inverse scattering problem inherently poses two main challenges: (i) non-linearity, since the scattered field is a non-linear function of the material properties and (ii) ill-posedness, since the integral operator has a smoothing effect and the number of measurements is finite in dimension and they are contaminated with noise. The non-linearity is tackled incorporating a multitude of techniques (ranging from Born approximation (linear), inexact Newton (linearized) to complete non-linear iterative Landweber schemes) that can account for weak to strong scattering problems. The ill-posedness of the EM inverse scattering problem is circumvented by formulating the above methods into a minimization problem with a sparsity constraint, which assumes that the dimension of the unknown object relative to the investigation domain is much smaller. Numerical experiments, which are carried out using synthetically generated measurements, show that the images recovered by these sparsity-regularized methods are sharper and more accurate than those produced by existing methods. The methods developed in this work have potential application areas ranging from oil/gas reservoir engineering to biological imaging where sparse domains naturally exist.
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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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  Ultraviolet band based underwater wireless optical communication

  Xiaobin Sun, Ph.D. Student, Electrical and Computer Engineering

  Apr 1, 10:00 - 12:00

  KAUST

  Optical communications optical wireless communicaitons communication networks photonics

  Underwater wireless optical communication (UWOC) has attracted increasing interest for data transfer in various underwater activities, due to its order-of-magnitude higher bandwidth compared to conventional acoustic and radio-frequency (RF) technologies. Our studies pave the way for eventual applications of UWOC by relieving the strict requirements on PAT using UV-based NLOS. Such modality is much sought-after for implementing robust, secure, and high-speed UWOC links in harsh oceanic environments. This work was first started with the investigation of proper NLOS configurations. Path loss (PL) was chosen as a figure-of-merit for link performance. The effects of NLOS geometries, water turbidity, and transmission wavelength are evaluated by measuring the corresponding PL. The experimental results suggest that NLOS UWOC links are favorable for smaller azimuth angles, stronger water turbidity, and shorter transmission wavelength, as exemplified by the use of 375-nm wavelength. With the understanding of favorable NLOS UWOC configurations, we established a NLOS link consisting of an ultraviolet (UV) laser as the transmitter for enhanced light scattering and high sensitivity photomultiplier tube (PMT) as the receiver. A high data rate of 85 Mbit/s using on-off keying (OOK) in a 30-cm emulated highly turbid harbor water is demonstrated. Besides the underwater communication links, UV-based NLOS is also appealing to be the signal carrier for direct communication across wavy water-air interface. The trial results indicate link stability, which alleviates the issues brought about by the misalignment and mobility in harsh environments, paving the way towards real applications.
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  Understanding a Block of Layers in Deep Neural Networks: Optimization, Probabilistic and Tropical Geometric Perspectives

  Adel Bibi, Ph.D., Electrical and Computer Engineering

  Mar 30, 18:00 - 20:00

  KAUST

  Computer Vision machine learning optimization

  In this dissertation, we aim at theoretically studying and analyzing deep learning models. Since deep models substantially vary in their shapes and sizes, in this dissertation, we restrict our work to a single fundamental block of layers that is common in almost all architectures. The block of layers of interest is the composition of an affine layer followed by a nonlinear activation function and then lastly followed by another affine layer. We study this block of layers from three different perspectives. (i) An Optimization Perspective. We try addressing the following question: Is it possible that the output of the forward pass through the block of layers highlighted above is an optimal solution to a certain convex optimization problem? As a result, we show an equivalency between the forward pass through this block of layers and a single iteration of certain types of deterministic and stochastic algorithms solving a particular class of tensor formulated convex optimization problems.
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  Exact Backstepping Control for Systems in Pure Feedback Form

  Prof. Johann Reger, Computer Science and Automation Faculty, TU Ilmenau, Germany

  Mar 29, 14:00 - 15:00

  KAUST

  Backstepping Lyapunov Theory non-linear feedback TU Ilmenau

  Traditional backstepping approaches may struggle to asymptotically stabilize systems in pure feedback form, due to its inherent implicit equations. Approximation based designs only have a limited domain of validity and turn out sensitive to model uncertainty and disturbances. We propose a new design that circumvents the necessity of solving implicit algebraic equations by introducing new state variables. Additional augmentations to the backstepping Lyapunov design lead to explicit expressions for the associated differential equations. The result is a dynamic state feedback, capable of asymptotically stabilizing the origin of a general class of nonlinear systems, based on just standard assumptions.
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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 28 - Mar 22, 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 21 - Mar 15, 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.
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  Part 2 - Introduction to backstepping control

  Prof. Johann Reger, Computer Science and Automation Faculty, TU Ilmenau, Germany.

  Mar 16, 09:00 - 11:30

  KAUST

  Backstepping TU Ilmenau Lyapunov Theory

  Backstepping is a widely applicable control technique based on Lyapunov theory that under rather mild assumptions leads to families of control laws for a large class of nonlinear systems. Focusing on systems of ordinary differential equations, we introduce the basic concept (integrator backstepping), generalize it, among others, to systems in strict feedback form and pure feedback form, which all enjoy an inherent controllability property, captured in the system structure. The course ends with extending the setting to the adaptive backstepping case, resorting to the certainty equivalence principle and Barbalat's lemma. The course is furnished by a series of exercises to let the students gather experience on tailored examples.
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  Light management in non-Hermitian media

  Dr. Waqas Ahmed, Electrical Engineering, King Abdullah University of Science and Technology

  Mar 15, 12:00 - 13:00

  KAUST

  Non-Hermitian Optics nanophotonics Artificial Structured Materials laser physics metamaterials

  Structured media provide the momentum compensation for the scattering of waves. It is well-known that nano-scale modulations of the refractive index may lead to a temporal and spatial control over light propagation. Yet, also engineering the gain and loss profile uncovers analogous shaping effects. However, only the interplay between both the refractive index and gain and loss modulations introduces unidirectionality in light management. Thus, non-Hermitian optics has become one of the most fertile grounds in optics. A generalized Hilbert transform allows tailoring the two quadratures of the complex permittivity to design periodic or disordered non-Hermitian media, holding either global or local unidirectionality following arbitrary vector fields to tailor the flow of light. The method allows restricting the permittivity within realistic values rendering it suitable for applications.

Mar 14 - Mar 8, 2020

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  Sensing, Localization, and Communications to Enable Future IoT Systems

  Tareq Al-Naffouri, Professor, Electrical and Computer Engineering

  Mar 12, 16:00 - 17:00

  KAUST

  IoT Sensor array processing

  In this talk, Tareq will present his research contributions and future directions to advance some critical IoT-enabling technologies: sensing, localization, and communications. He will demonstrate how we take advantage of structure in sensed-data and sensor arrays. This structure can help mitigate sensing uncertainties, improve localization accuracy, and enhance the performance of communication systems, all while reducing the computational overhead. The Internet of Things (IoT) has ushered a new era in many fields including retail, medicine, agriculture, and the automotive industry. In fact, it is projected that by 2025, one trillion IoT devices will be deployed worldwide: the equivalent of 1000 devices per person. To reach such a scale, major advancements are needed in various IoT-enabling technologies.
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  Functional data depth and its application in the visualization of spatio-temporal covariance structures

  Mar 12, 12:00 - 13:00

  KAUST

  functional data analysis spatio-temporal statistics

  Functional data analysis is a very active research area due to the overwhelming existence of functional data. In the first part of this talk, I will introduce how functional data depth is used to carry out exploratory data analysis and explain recently-developed depth techniques. In the second part, I will discuss spatio-temporal statistical modeling. It is challenging to build realistic space-time models and assess the validity of the model, especially when datasets are large. I will present a set of visualization tools we developed using functional data analysis techniques for visualizing covariance structures of univariate and multivariate spatio-temporal processes. I will illustrate the performance of the proposed methods in the exploratory data analysis of spatio-temporal data.
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  Part 1 - Introduction to backstepping control

  Prof. Johann Reger, Computer Science and Automation Faculty, TU Ilmenau, Germany.

  Mar 12, 09:00 - 11:30

  KAUST

  Backstepping TU Ilmenau

  Backstepping is a widely applicable control technique based on Lyapunov theory that under rather mild assumptions leads to families of control laws for a large class of nonlinear systems. Focusing on systems of ordinary differential equations, we introduce the basic concept (integrator backstepping), generalize it, among others, to systems in strict feedback form and pure feedback form, which all enjoy an inherent controllability property, captured in the system structure. The course ends with extending the setting to the adaptive backstepping case, resorting to the certainty equivalence principle and Barbalat's lemma. The course is furnished by a series of exercises to let the students gather experience on tailored examples.
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  Radio Frequency Sensing in Healthcare: Detection of Falls, Gait Abnormality and Vital Signs

  Moeness Amin, Professor and Director of the Center for Advanced Communications, Villanova University, USA

  Mar 8, 12:00 - 13:00

  B9 L2 H1

  Radio frequency devices sensor healthcare

  In this talk, we represent recent advances of radio frequency (RF) sensing technology for healthcare, specifically in monitoring human activities inside homes, retirement facilities, and hospitals. Sensing technologies and data analytics are considered powerful tools in efficient indoor monitoring of human activities. Monitoring of activities of daily living (ADL) can identify falls, which are considered as the leading cause of fatal and non-fatal injuries for people aged 65 and over. It can also detect variants in activity patterns and changes in routines and lifestyle as well as the state of physical, cognitive, and psychological health of the person. In addition to monitoring ADL, RF-based gesture recognition using hands and arms is shown to be an important contactless technology for Man-Machine-Interface (MMI). Adding to the indoor applications, RF-based vital sign monitoring has vast medical use, as respiration and heartbeats are essential diagnostic barometers for many health problems. More recently, RF sensors have also been proposed for gait analysis for rehabilitation and timely diagnosis of many neurological, orthopedic and medical conditions. Changes in gait patterns can also be precursors of falls. In this talk, we present successful examples in each of the above application areas and discuss pertinent open problems worthy of investigations.

Mar 7 - Mar 1, 2020

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  Simulation of Neuronal Signal Processing - 2020-03-05

  Gabriel Wittum, Professor (former), Applied Mathematics and Computational Science

  Mar 5, 12:00 - 13:00

  B9 L2 R2322

  Neuronal ensembles Stochastiic Variability neuroscience

  In the lecture we present a three dimensional mdoel for the simulation of signal processing in neurons. To handle problems of this complexity, new mathematical methods and software tools are required. In recent years, new approaches such as parallel adaptive multigrid methods and corresponding software tools have been developed allowing to treat problems of huge complexity. 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.
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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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  Dual Function Radar Communication Systems: Towards RF Convergence

  Moeness Amin , Professor and Director of the Center for Advanced Communications, Villanova University, USA

  Mar 2, 14:00 - 17:00

  B1 L3 R3119

  radar

  This tutorial establishes and promotes the area of dual system functionality, allowing the radar to house voice and data transmission, leading to technological advances in radar and communications systems. The tutorial develops novel signaling schemes for embedding information into the radar pulsed emissions which, in most cases, is blind to the primary radar operation and radar ambiguity function. It considers different antenna configurations, including multiple-input multiple-output (MIMO) radars and shows how to achieve high data rate communications by combining amplitude-shift keying, phase-shift keying, and code shift keying modulations with waveform-diversity and spatial degrees of freedom.
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  Data-sparse Methods for Large-scale Applications on Emerging Architectures

  David Keyes, Professor, Applied Mathematics and Computational Science

  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.
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  Engineering Organic Electronics for Biology - 2020-03-01

  Sahika Inal, Assistant Professor, Bioengineering, KAUST

  Mar 1, 12:00 - 13:00

  B9 L2 H1 R2322

  bioelectronics

  The field of bioelectronics combines the worlds of electronics and biology with the aim of developing new tools for biomedical research and healthcare. The majority of implantable devices are mechanically stiff and the mechanical properties mismatch with soft tissue causes an immune response which results in their rejection from the body. Another limitation is associated with the fact that most devices utilize metal electrodes to record from/stimulate tissue. These electrodes offer limited coupling with ion fluxes used by cells to communicate with each other, resulting in low efficiency. Such challenges can be overcome with the integration of soft, conducting polymers displaying mixed (ionic and electronic) conduction. In this talk, I will present approaches that leverage the properties of organic conducting materials in order to develop bioelectronic devices interfacing with the body. These devices include organic electrochemical transistors for measuring metabolites, neural activity and integrity of cellular layers.

Feb 29 - Feb 23, 2020

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  Recent Advances in Sparse Array Design

  Moeness Amin , Professor and Director of the Center for Advanced Communications, Villanova University, USA

  Feb 27, 14:00 - 17:00

  B1 L3 R3119

  sparse array

  In this tutorial, we review sparse arrays from the coarray perspective that strives for full augment ability, i.e., maximizing the number of spatial autocorrelation lags. In this respect, we discuss sparse array performance for direction finding and also address the passive and active arrays for stationary and moving platforms. We then contrast these configurations with sparse arrays that achieve MaxSINR for both narrowband and wideband sources operating in an interference-active environment. The tutorial also considers both single point source and multiple point sources. We cover the two important cases where the array aperture size is constrained and unconstrained and demonstrate optimum performance in both cases. For the former, and with a limited aperture, we introduce a hybrid design that seeks a full augmentable array which at the same time optimizes beamformer performance. The problem is formulated as a quadratically constraint quadratic program, with the cost function penalized with weighted l1-norm squared of the beamformer weight vector. The wideband problem is tackled by two different approaches, one includes a delay line filter implementation and the other one is the DFT approach.
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  Towards Collaboration of Multiple Intelligent Robots and Human Supervision

  Dr. Inmo Jang, Postdoctoral Researcher, Robotics for Extreme Environment Group at the University of Manchester

  Feb 27, 10:00 - 11:00

  B2 L5 R5209

  robotics game theory algorithms

  As autonomy in individual robots becomes advanced, one of the next challenges is to coordinate multiple of such intelligent robots, which are then expected to innovatively transform legacy industries (e.g., warehouse automation, connected-vehicle management, etc.). Towards collaboration of multiple robots, this talk will particularly introduce a game-theoretical framework for clustering a large number of multiple robots and assigning the robot teams to given tasks, where the network of the robots is strongly connected and the individuals are asynchronous. The proposed decentralised algorithm guarantees convergence of selfish agents having social inhibition towards a Nash stable partition (i.e., social agreement) within polynomial time.
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  Deterministic particle approximation for nonlinear transport equations

  Marco Di Francesco, Associate Professor, Department of Information Engineering, Computer Science and Mathematics, University of L'Aquila (Italy)

  Feb 25, 14:00 - 15:00

  Building L3 R3119

  PDE numerical methods nonlinear models

  Approximating the solution to an evolutionary partial differential equation by a set of "moving particles" has several advantages. It validates the use of a continuity equation in an "individuals-based" modeling setting, it provides a link between Lagrangian and Eulerian description, and it defines a "natural" numerical approach to those equations. I will describe recent rigorous results in that context. The main one deals with one-dimensional scalar conservation laws with nonnegative initial data, for which we prove that the a suitably designed "follow-the-leader" particle scheme approximates entropy solutions in the sense of Kruzkov in the many particle limit. Said result represents a new way to solve scalar conservation laws with bounded and integrable initial data. The same method applies to second order traffic flow models, to nonlocal transport equations, and to the Hughes model for pedestrian movements.
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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.
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  Manipulating Magnetization with Acoustic Waves

  Pallavi Dhagat, Professor, Electrical Engineering and Computer Science, Oregon State University, USA

  Feb 23, 12:00 - 13:00

  B9 L2 H1

  magnetization acoustic waves

  In this seminar, I will present our work on manipulating magnetization with acoustic waves. The temporally and spatially varying strain in acoustic waves produces a corresponding change in the local anisotropy of magnetostrictive materials through the Villari effect. This magneto-acoustic coupling may be used for patterning magnetic films and for nonlinear signal processing such as amplification and correlation of spin waves. I will discuss our experiments and results towards these application possibilities, and also present the techniques we have developed to characterize magnetostriction.

Feb 22 - Feb 16, 2020

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  Statistical models for long-range dependent climate data

  Sigrunn Sorbye, Associate Professor, UiT The Arctic University of Norway

  Feb 20, 12:00 - 13:00

  B9 L2 R2322

  statistical models climate data

  In this talk I will discuss statistical models which incorporate temperature response to the radiative forcing components. The models can be used to estimate important climate sensitivity measures and give temperature forecasts. Bayesian inference is obtained using the methodology of integrated nested Laplace approximation and Monte Carlo simulations. The resulting approach will be demonstrated in analyzing instrumental data and Earth system model ensembles.
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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, Professor, 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.
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  Distributed Multi-parameter Sensing Using a Multimode Fiber

  Islam Ashry, Senior Research Scientist, Photonics Laboratory

  Feb 16, 12:30 - 13:00

  B9 L2 H1 R2322

  Graduate Seminar Part 2. Fiber-optic distributed acoustic sensor (DAS) and distributed temperature sensor (DTS) are considered important for many applications. It is challenging to design a hybrid DAS-DTS system using the same optical fiber because the operation principles of the two sensors are different. In this talk, we summarize the concept of using the widespread standard multimode fiber (MMF) for simultaneous distributed acoustic and temperature sensing. In particular, we operate the MMF in a quasi-single-mode (QSM) state to simultaneously fulfill the functional requirements of the DAS and DTS. This technique is significant for many industrial applications because it efficiently tackles a long-standing issue in practical implementation.
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  Energy and Entropy in Numerical Methods

  Hendrik Ranocha, Postdoctoral Research Fellow, Applied Mathematics and Computational Science

  Feb 16, 12:00 - 13:00

  B9 L2 H2

  numerical methods

  Secondary quantities such as energy and entropy can be very important for numerical methods. Firstly, preserving these quantities can ensure that non-physical behavior is excluded. Secondly, preserving such quantities can result in stability estimates. Finally, preserving the correct energy/entropy evolution in time can result in additional desirable properties such as lower numerical errors. In this talk, a brief overview of some recent advances concerning energy and entropy preserving numerical methods for ordinary and partial differential equations will be given, together with an outlook on future research directions and applications.