Monday, March 30, 2020, 18:00
- 20:00
https://kaust.zoom.us/j/279877360
Contact Person
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.
Prof. Johann Reger, Computer Science and Automation Faculty, TU Ilmenau, Germany
Sunday, March 29, 2020, 14:00
- 15:00
https://kaust.zoom.us/j/738675308
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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.
Sunday, March 29, 2020, 12:00
- 13:00
https://kaust.zoom.us/j/891114016
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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.
Di Wang, Ph.D. Student, Computer Science and Engineering, State University of New York at Buffalo
Tuesday, March 24, 2020, 15:00
- 16:00
https://kaust.zoom.us/j/914184907
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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.
Riyadh Baghdadi, Postdoctoral Associate, Computer Science, MIT
Sunday, March 22, 2020, 15:00
- 16:00
https://kaust.zoom.us/j/213165634
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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.
Monday, March 16, 2020, 12:00
- 13:00
https://kaust.zoom.us/j/537638698
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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. To join the online event, go to https://kaust.zoom.us/j/537638698 .
Prof. Johann Reger, Computer Science and Automation Faculty, TU Ilmenau, Germany.
Monday, March 16, 2020, 09:00
- 11:30
https://kaust.zoom.us/j/6097347922
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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. To join the course please go to https://kaust.zoom.us/j/6097347922 .
Dr. Waqas Ahmed, Electrical Engineering, King Abdullah University of Science and Technology
Sunday, March 15, 2020, 12:00
- 13:00
https://kaust.zoom.us/j/4807265666
Contact Person
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. To join the seminar please go to https://kaust.zoom.us/j/4807265666.
Thursday, March 12, 2020, 16:00
- 17:00
https://kaust.zoom.us/j/290163769
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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. To join the seminar please go to https://kaust.zoom.us/j/290163769 .
Thursday, March 12, 2020, 12:00
- 13:00
https://kaust.zoom.us/j/255432702
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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. To join the event please go to https://kaust.zoom.us/j/255432702 .
Prof. Johann Reger, Computer Science and Automation Faculty, TU Ilmenau, Germany.
Thursday, March 12, 2020, 09:00
- 11:30
https://kaust.zoom.us/j/638226292
Contact Person
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. To join the seminar please go to https://kaust.zoom.us/j/638226292 .
Moeness Amin, Professor and Director of the Center for Advanced Communications, Villanova University, USA
Sunday, March 08, 2020, 12:00
- 13:00
Building 9, Level 2, Hall 1
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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.
Thursday, March 05, 2020, 12:00
- 13:00
Building 9, Level 2, Room 2322
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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.
Tuesday, March 03, 2020, 10:00
- 11:30
Building 9, Level 2, Hall 2, Room 2325
Contact Person
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.
Moeness Amin , Professor and Director of the Center for Advanced Communications, Villanova University, USA
Monday, March 02, 2020, 14:00
- 17:00
Building 1, Level 3, Room 3119
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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.
Monday, March 02, 2020, 12:00
- 13:00
Building 9, Level 2, Hall 1, Room 2322
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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.
Sahika Inal, Assistant Professor, Bioengineering, KAUST
Sunday, March 01, 2020, 12:00
- 13:00
Building 9, Level 2, Hall 1, Room 2322
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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.
Moeness Amin , Professor and Director of the Center for Advanced Communications, Villanova University, USA
Thursday, February 27, 2020, 14:00
- 17:00
Building 1, Level 3, Room 3119
Contact Person
 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. 
Dr. Inmo Jang, Postdoctoral Researcher, Robotics for Extreme Environment Group at the University of Manchester
Thursday, February 27, 2020, 10:00
- 11:00
Building 2, Level 5, Room 5209
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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.
Marco Di Francesco, Associate Professor, Department of Information Engineering, Computer Science and Mathematics, University of L'Aquila (Italy)
Tuesday, February 25, 2020, 14:00
- 15:00
Building, Level 3, Room 3119
Contact Person
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.
Charalambos (Harrys) Konstantinou, Assistant Professor of Electrical and Computer Engineering with Florida A&M University and Florida State University (FAMU-FSU) College of Engineering
Monday, February 24, 2020, 12:00
- 13:00
Building 9, Level 2, Hall 1
Contact Person
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.
Pallavi Dhagat, Professor, Electrical Engineering and Computer Science, Oregon State University, USA
Sunday, February 23, 2020, 12:00
- 13:00
Building 9, Level 2, Hall 1
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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. 
Sigrunn Sorbye, Associate Professor, UiT The Arctic University of Norway
Thursday, February 20, 2020, 12:00
- 13:00
Building 9, Level 2, Room 2322
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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.
Takashi Gojobori, Distinguished Professor, Bioscience
Wednesday, February 19, 2020, 12:00
- 13:00
Building 9, Level 2, Hall 2 (Room 2325)
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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.