Events

Feb 27 - Mar 5, 2022

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  Towards Robotic Spatial Awareness with Unsupervised Lifelong Learning

  Chen Wang, Project Scientist, Robotics Institute, Carnegie Mellon University (CMU)

  Feb 28, 14:30 - 15:30

  KAUST

  People have used robots for more than six decades to empower people to do things that are typically dirty, dull, or dangerous. The industry has also progressed significantly over the period from basic mechanical assist systems to autonomous cars, environmental monitoring, and exploration of outer space. Despite those achievements, the robots still cannot work like human being because of lacking real spatial awareness. For instance, humans can easily tidy up a messy room, while robots even have difficulty to re-identify a novel toy misplaced. To solve this problem, we need the robot to have human-like knowledge and spatial awareness, which refers to being aware of our surroundings and our state relative to them. Spatial perception consists of two processes, the exteroceptive process, which creates representations about space through feelings and learning and interoceptive process, which creates a mechanism to memorize the experience as well as cognizing information about our body, such as position and orientation. In this talk, I will preset our recent work towards robotic spatial awareness, including robust space representation, visual memorability, lifelong learning ability, and self-motion cognition. We expect that the ability of spatial awareness will be able to promote the robots to automatically adjust to stochastic, dynamic, and non-stationary environments.
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  Filling the Resources Gap with Approximate Mobile Computing

  Veljko Pejović, Assistant professor at the Faculty of Computer and Information Science (UL FRI), University of Ljubljana, Slovenia

  Feb 28, 12:00 - 13:00

  B9 R2322 H1

  Mobile computing proliferation is critically threatened by the breakdown of Dennard scaling, a law describing the area-proportional growth of integrated circuit power use.
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  The KAUST Research Conference on Robotics and Autonomy 2022

  Eric Feron, Professor, Electrical and Computer Engineering

  Feb 28, 08:00 - Mar 2, 20:00

  B19 H1

  robotics

  KAUST Robotics, Intelligent Systems and Control Lab (RISC Lab) will host the KAUST Research Conference on Robotics and Autonomy 2022 (#RobotoKAUST) from February 28 until March 2, 2022. The conference will address the most recent trends of robotics application in a range of disciplines. To attend RobotoKAUST Gala, please, read more about the event and follow the event registration instructions.
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  Modelling waves with structure-preserving numerical discretizations

  David Ketcheson, Professor, Applied Mathematics and Computational Sciences

  Feb 27, 14:00 - 16:00

  B9 L2 H2

  Models of physical phenomena include important qualitative properties, and any useful approximate solution of the model must respect these properties. Such properties include conservation or dissipation of energy, as well as positivity of quantities like mass, probability, or concentration. Preservation of these properties in computationally affordable numerical solutions of complex physical models remains a major challenge today. I will describe some recent advances in numerical methods for general dynamical systems that enable preservation of system dynamics and of bounds on the state, in the context of high-order accurate and efficient discretizations. The power of these methods will be demonstrated through applications in the area of surface water waves.
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  Drilling Mechanics: Bit Sensing and Physics-Based Modelling for Drilling Optimization

  Alexis Koulidis, Ph.D. Student, Physical Science and Engineering

  Feb 27, 12:00 - 13:00

  B9 L2 R2322 H1

  Drilling Mechanics

  Challenging drilling applications and fluctuating oil prices have created a new emphasis on developing innovative technology to enhance safety and reduce cost. Polycrystalline Diamond Compact (PDC) drill bits were introduced in the late 1970s and revolutionized the drilling industry, with currently 60% of the drill bits utilized in oil and gas wells being PDC.

Feb 20 - Feb 26, 2022

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  Combined analysis of spatially misaligned data using Gaussian fields and the stochastic partial differential equation approach

  Feb 24, 12:00 - 13:00

  B9 L2 H2

  Spatially misaligned data are becoming increasingly common due to advances in both data collection and management in a wide range of scientific disciplines including the epidemiological, ecological and environmental fields. Here, we present a Bayesian geostatistical model for fusion of data obtained at point and areal resolutions. The model assumes that underlying all observations there is a spatially continuous variable that can be modeled using a Gaussian random field process.
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  Computer Algebra in Scientific Computing

  Dmitry Lyakhov, Research Scientist, Applied Mathematics and Computational Sciences

  Feb 21, 12:00 - 13:00

  B9 R2322 H1

  In this talk I will speak about some recent theoretical advances in symbolic computation. In particular, exact computations with polynomials and differential equations will be discussed.
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  Extreme Statistics: Theory and Methods for Modeling Rare High-Impact Events in Complex Settings

  Raphaël Huser, Associate Professor, Statistics

  Feb 20, 16:00 - 17:30

  B4 B5 A0215

  Rare, low-probability events often lead to the biggest impacts. The goal of the Extreme Statistics (extSTAT) research group at KAUST is to develop cutting-edge statistical approaches for modeling, predicting and quantifying risks associated with these extreme events in complex systems arising in various scientific fields, such as climate science and finance. In particular, the work that we develop and continue to refine has a direct potential impact to climate scientists and related stakeholders, such as engineers and insurers, who have realized that under climate change, the greatest environmental, ecological, and infrastructural risks and damages, are often caused by changes in the intensity, frequency, spatial extent, and persistence of extreme events, rather than changes in their average behavior. However, while datasets are often massive in modern day applications, extreme events are always scarce by nature. This makes it very challenging to provide reliable risk assessment and prediction, especially when extrapolation to yet-unseen levels is required. To overcome these existing limitations, the extSTAT research group develops novel methods that transcend classical extreme-value theory to build new resilient statistical models, as well as computationally efficient inference methods, which improve the prediction of rare events in complex, high-dimensional, spatio-temporal, non-stationary settings. In this talk, I will provide an overview of my group's recent research activities and future directions with a focus on core statistical methodology contributions. The technical part of the talk will describe selected research highlights, which include (but are not limited to) the development of new flexible sub-asymptotic models applied to assessing contagion risk among leading cryptocurrencies, the development of a novel low-rank spatial modeling framework applied to estimating extreme hotspots in high-resolution Red Sea surface temperature data, and the development specialized spatio-temporal point process models applied to predicting devastating rainfall-induced landslides in a region of Italy. I will conclude my talk with an outlook on my future research plans. Motivated by methodological obstacles that arise with “big models” for complex extremes data, as well as new substantive challenges in collaborative work at KAUST, we will embark on the development of fundamentally superior models that have an intrinsically sparse probabilistic structure, as well as new "hybrid" methods that combine the strength of (parametric) models from extreme-value theory with the pragmatism and predictive power of (nonparametric) machine learning algorithms, thus opening the door to interpretable and “extreme-ly” accurate predictive models for rare events in unprecedented dimensions.
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  When Second Order Methods Shine: Big Batches, Bayes, and Bilevel

  Guodong Zhang, PhD student in University of Toronto

  Feb 20, 15:00 - 16:00

  KAUST

  In this talk, I will discuss how the use of second-order information – e.g, curvature or covariance – can help in all three problems, yet with vastly different roles in each. First, I will present a noisy quadratic model, which qualitatively predicts scaling properties of a variety of optimizers and in particular suggests that second-order optimization algorithms would extend perfect scaling to much bigger batches. Second, I will show how we can derive and implement scalable and flexible Bayesian inference algorithms from standard second-order optimization algorithms. Third, I will describe a novel second-order algorithm that finds desired equilibria and save us from converging to spurious fixed points in two-player sequential games (i.e. bilevel optimization) or even more general settings. Finally, I will conclude how my research would pave the way towards intelligent machines that can learn from data and experience efficiently, reason about their own decisions, and act in our interests.
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  Multi-physics Simulation of Plasmonic Photoconductive Devices and Antennas

  Hakan Bagci, Associate Dean for Students, Computer, Electrical and Mathematical Sciences and Engineering

  Feb 20, 12:00 - 13:00

  B9 L2 R2322 H1

  Multiphysics Simulation

  Terahertz (THz) frequency electromagnetic fields have numerous applications ranging from wireless communications to imaging systems and nondestructive testing, to material characterization. One of the main obstacles in the way of widespread industrial use of THz technologies is the difficulty of implementing compact and frequency-stable THz sources that can operate at room temperatures. Among a variety of possible options, photoconductive devices (PCDs) satisfy these conditions. Indeed, they have become one of the most promising candidates for THz source generation since recent advances in fabrication techniques, such as metasurface integration and nanostructured surface inclusions have significantly increased their optical-to-THz conversion efficiency and made them polarization insensitive.

Feb 13 - Feb 19, 2022

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  Modelling and Simulation of Biological Systems - 2022-02-17

  Gabriel Wittum, Professor, Applied Mathematics and Computational Sciences

  Feb 17, 12:00 - 13:00

  B9 L2 H2

  Biological systems are distinguished by their enormous complexity and variability. That is why mathematical modelling and computational simulation of those systems is very difficult, in particular thinking of detailed models which are based on first principles. The difficulties start with geometric modelling which needs to extract basic structures from highly complex and variable phenotypes, on the other hand also has to take the statistic variability into account.
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  Causal reasoning in settings with competing events and truncation by death

  Prof. Mats Julius Stensrud, Department of Mathematics, EPFL

  Feb 16, 16:00 - 17:00

  B9 L2 R2322

  A competing event is any event that makes it impossible for the outcome of interest to occur. The presence of competing events requires us to be careful about the interpretation of classical causal estimands. In particular, the average treatment effect captures effects through the competing event, pathways that may not be of primary interest. As a solution, we suggest the separable effect, inspired by Robins and Richardson’s extended graphical approach. We will give criteria that allow different interpretations of the separable effects and present identification conditions that can be evaluated in causal graphs.
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  Intermediate Distributions and Complexity Bounds for SMC

  Prof. Anthony Lee, School of Mathematics, University of Bristol

  Feb 16, 15:00 - 16:00

  KAUST

  It is now fairly common to use Sequential Monte Carlo (SMC) algorithms for normalizing constant estimation of high-dimensional, complex distributions without any particular structure. In order for the algorithm to give reasonable accuracy, it is well known empirically that one must introduce appropriate intermediate distributions that allow the particle system to “gradually evolve” from a simple initial distribution to the complex target distribution, and one must also specify an appropriate number of particles to control the error. Since both the number of intermediate distributions and the number of particles affect the computational cost of the algorithm, it is crucial to study and attempt to minimize the computational cost of the algorithm subject to constraints on the error.
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  Security and Resiliency for Critical Infrastructures

  Professor Roberto Di Pietro, College of Science and Engineering, Cybersecurity at Hamad Bin Khalifa University

  Feb 14, 12:00 - 13:00

  B9 L2 R2322 H1

  Our standard of living, nation GDP, and, in some cases, even our safety rely on critical infrastructures (CIs). In particular, being CIs generally perceived as a commodity (think of GPS availability, or avionics & maritime traffic routes and hubs), their security has largely been overlooked. The emergent property is that, nowadays, CIs systems are generally fragile, especially with respect to cyber attacks.
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  Fully-integrated gas sensing systems and self-powered NO₂ gas sensors

  Mani Teja Vijjapu, Ph.D. Student, Electrical and Computer Engineering

  Feb 13, 12:00 - 13:00

  B9 L2 R2322 H1

  Fully integrated gas sensing systems

  Mobile devices for the personalized detection of health and environmental hazards are becoming the basis for futuristic sensing technologies.

Feb 6 - Feb 12, 2022

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  Exploring Spectral Dependence in Multivariate Time Series

  Hernando Ombao, Professor, Statistics

  Feb 10, 12:00 - 13:00

  KAUST

  Advances in imaging technology have given neuroscientists unprecedented access to examine various facets of how the brain “works”. Brain activity is complex. A full understanding of brain activity requires careful study of its multi-scale spatial-temporal organization (from neurons to regions of interest; and from transient events to long-term temporal dynamics). Motivated by these challenges, we will explore some characterizations of dependence between components of a multivariate time series and then apply these to the study of brain functional connectivity. This is potentially interesting for brain scientists because functional brain networks are associated with cognitive function and mental and neurological diseases.
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  Phase Separation in Heterogeneous Media

  Irene Fonseca, Center for Nonlinear Analysis, Carnegie Mellon University

  Feb 8, 19:00 - 20:00

  KAUST

  A variational model in the context of the gradient theory for fluid-fluid phase transitions with small scale heterogeneities is studied. In the case where the scale of the small heterogeneities is of the same order of the scale governing the phase transition, the interaction between homogenization and the phase transitions process leads to an anisotropic interfacial energy. Bounds on the homogenized surface tension are established. In addition, a characterization of the large-scale limiting behavior of viscosity solutions to non-degenerate and periodic Eikonal equations in half-spaces is given. This is joint work with Riccardo Cristoferi (Radboud University, The Netherlands), Adrian Hagerty (USA), Cristina Popovici (USA), Rustum Choksi (McGill, Canada), Jessica Lin (McGill, Canada), and Raghavendra Venkatraman (NYU, USA).
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  Procedural Modeling of Botanical Trees

  Wojtek Palubicki is leading the Natural Phenomena Modeling Group at the Faculty of Mathematics and Compter Science at the Adam Mickiewicz University in Poznan.

  Feb 7, 12:00 - 13:00

  KAUST

  The exact mechanisms controlling plant growth are a topic of ongoing botanical research. This makes the realistic computer simulation of plants a challenging task. We present a method for generating realistic models of trees and shrubs. This method is based on the biological hypothesis that the form of a developing tree emerges from a self-organizing process dominated by the competition of buds and branches for light or space, and regulated by internal signaling mechanisms. Simulations of this process robustly generate a wide range of realistic trees and bushes.
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  Numerical analysis, physics, and high-performance computing: A firm three-hand shake for next-generation predictive computational fluid dynamics frameworks.

  Matteo Parsani, Associate Professor, Applied Mathematics and Computational Sciences

  Feb 6, 14:00 - 16:00

  B4 L5 R5220

  Together with the algorithm suitability to exploit current petascale and next-generation exascale supercomputers, robust, accurate, and structure-preserving discretizations are necessary for developing predictive computational tools. The research carried out in the Advanced Numerical Algorithms and Numerical Simulations Laboratory (AANSLab) leverages a multidisciplinary platform that integrates numerical analysis, physics, and high-performance computing. In particular, we focus on the analysis and development of novel numerical methods for ordinary and partial differential equations with provable properties such as nonlinear stability and conservation, and structure-preserving techniques. These properties are critical for designing reliable, efficient, and self-adaptive solvers for complex geometries – an essential cornerstone for next-generation computational frameworks. Current classes of partial differential equations that we are working on are the compressible Navier–Stokes equations, the Eulerian model for compressible heat-conducting flows, and the diffusion-reaction and convection-diffusion-reaction equations for molecular communication. We also use deep learning to complement and speed up the process of solving efficiently large-scale PDE-based problems. In this talk, I will summarize the progress we made in the last five years in the following areas: - Numerical analysis and algorithm development for robust, smart compressible flow solvers. - Development from the ground up of a new scalable hp-adaptive computational fluid dynamics (CFD) framework that places KAUST a few years ahead of the NASA CFD 2030 vision: o Applications and impact in the automotive and aerospace industry. o Improving knowledge of flow physics: Examples in detonation and aeroacoustics. - Advection-reaction-diffusion algorithms for molecular communication. Finally, I'll discuss our translational work to solve industrially relevant flow problems in partnership with Boeing, NASA Langley Research Center, and McLaren F1 Racing Team, and my future research plans.
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  Safe and Secure Computer Control Systems? The devil is in the details...

  Feb 6, 12:00 - 13:00

  B9 L2 R2322 H1

  In modern societies, there is an accelerated convergence of information technology (IT) like the internet-cloud-web complex, with operational technology (OT) like cyber-physical systems (CPS) control, complemented by Internet-of-Things fabrics (IoT).

Jan 30 - Feb 5, 2022

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  Fully-integrated systems and self-powered gas sensors for sustainable environment monitoring

  Mani Teja Vijjapu, Ph.D. Student, Electrical and Computer Engineering

  Feb 3, 13:30 - 15:00

  B3 L5 R5209

  Mobile devices for the personalized detection of health and environmental hazards are becoming the basis for futuristic sensing technologies. In recent decades, air and environmental pollution levels have risen globally. Therefore, environmental protection must be strengthened by developing sensors that detect pollutants. The monitoring of these pollutants with high spatial coverage requires inexpensive electronic gas sensors and self-sustainable sensing systems that can be deployed everywhere. This dissertation reports on technological developments to provide solutions for inexpensive, compact, power-efficient, and easily deployable toxic gas sensors and integrated systems using semiconducting metal-oxide thin-film transistors (TFTs).
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  A Non-stationary Autologistic Model for Space-time Data

  Yiping Hong, Postdoctoral Research Fellow, Statistics

  Feb 3, 12:00 - 13:00

  KAUST

  Abstract In many research fields such as meteorology, ecology, and epidemiology, the spatio-temporal datasets are binary, describing the existence of particular species or events. The spatial dependence of the binary observations often exhibits nonstationarity. However, most nonstationary models in the literature are based on Gaussian random fields, which may be computational demanding. We propose a nonstationary spatio-temporal autologistic regression model, which allows the spatial covariances to vary in space. We investigate the spatial and temporal correlation of autologistic models with
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  Exoplanet Imaging Coronagraphic Mask Development and Testing: A North American Perspective

  Benjamin L. Gerard, Postdoctoral Scholar, University of California, UC Observatories

  Feb 2, 16:30 - 17:30

  KAUST

  Over 4000 exoplanets - planets beyond the Solar System - have been discovered since the first Nobel prize-winning exoplanet detection around a Sun-like star in 1995. The majority of these exoplanets have been detected by indirect methods, inferring the presence of the exoplanet by observing the star.
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  Elliptic PDEs: Regularity under minimal assumptions - Part 3

  Prof. Giuseppe Di Fazio, Department of Mathematics, University Of Catania, Italy

  Feb 2, 15:00 - 17:00

  B1 L4 R4102

  Elliptic PDEs are ubiquitous both in Mathematics and in applications of Mathematics. Regularity of generalized solutions is a fundamental issue necessary to handle in proper way if one want to obtain qualitative information about solutions. My goal is to introduce the audience to the topic of regularity for elliptic PDEs under assumptions on the coefficients that are of minimal requirements.
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  Climate variability and change in the past, present, and future: complementary insights from models, satellites, and ground stations

  Dr. Hylke Beck, Senior Scientist, Joint Research Centre (JRC), European Commission, Italy

  Feb 2, 10:15 - 11:15

  B2 L5 R5220

  climate variability

  Extreme climate events affect the lives of millions people around the world each year and are anticipated to increase in frequency and magnitude due to climate change. Improving our understanding of historical and expected climate variability and change is paramount to mitigating the impacts of these extreme events. In this seminar, I will talk about my research on the fusion of information from weather models, satellites, and ground stations to gain a better understanding of climate variability in the past, present, and future.
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  Elliptic PDEs: Regularity under minimal assumptions - Part 2

  Prof. Giuseppe Di Fazio, Department of Mathematics, University Of Catania, Italy

  Jan 31, 15:00 - 17:00

  B1 L4 R4214

  Elliptic PDEs are ubiquitous both in Mathematics and in applications of Mathematics. Regularity of generalized solutions is a fundamental issue necessary to handle in proper way if one want to obtain qualitative information about solutions. My goal is to introduce the audience to the topic of regularity for elliptic PDEs under assumptions on the coefficients that are of minimal requirements.
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  New tools for symbolic computation in partial differential equations

  Diogo Gomes, Program Chair, Applied Mathematics and Computational Sciences

  Jan 31, 12:00 - 13:00

  KAUST

  The qualitative study of PDEs often relies on integral identities and inequalities. For example, for time-dependent PDEs, conserved integral quantities or quantities that are dissipated play an important role. In particular, if these integral quantities have a definite sign, they are of great interest as they may provide control on the solutions to establish well-posedness.
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  On-Chip Antennas: The Last Barrier to True RF System-on-Chip

  Atif Shamim, Program Chair, Electrical and Computer Engineering

  Jan 30, 12:00 - 13:00

  KAUST

  Antennas are integral part of wireless communication devices and traditionally have remained off the Integrated Circuits (ICs which are also commonly known as chips) resulting in large sized modules.

Jan 23 - Jan 29, 2022

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  Modulating Functions Based State and Parameter Estimation for Robotic Systems

  Matti Noack, Research Fellow, Control Engineering, Technische Universität Ilmenau

  Jan 27, 13:00 - 14:00

  B1 L4 R4214

  Robotic systems are well-known to be highly nonlinear processes due to their complex kinematics. The identification of the corresponding system dynamics plays an important role in obtaining an adequate model which is especially hard under changing parametric circumstances. Furthermore, precise knowledge of the state is crucial for a large variety of control tasks. Sparse sensor setups make these problems more challenging due to significant noise impact. For designing an efficient and robust algorithm, an integral transform approach is proposed exploiting the robotic system structure. Specifically, the Modulating Function Method is introduced in the context of multi-body systems for fixed-time parameter and state estimation. An adaptive observer structure is presented in order to give an impression of the general methodology and the related research questions.
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  Designing a Stable Periodic Orbit for a Triple Pendulum on a Cart

  Prof. Johann Reger, Automation and Systems Engineering, Technische Universität Ilmenau

  Jan 27, 12:00 - 13:00

  B1 L4 R4214

  In this talk an inversion-based control approach is presented for the generation and stabilization of a periodic orbit in a multi-link triple pendulum on a cart. To this end, a nominal trajectory is generated by formulating the posture transition problem as a two-point Boundary Value Problem (BVP) in an input-output representation. For solvability of the BVP, a setup function is introduced such that additional parameters are provided in the differential equation of the internal dynamics. Based on the linearized dynamics about the nominal trajectory, a linear-quadratic-Gaussian controller is implemented to compensate for measurement noise, model uncertainties, and external disturbances. This way we force a triple pendulum to move along a non-trivial periodic orbit and render it attractive. The high performance and accuracy of our approach is illustrated on an experimental setup.
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  Backward induction: from theory to empirics

  Konrad Grabiszewski, Instructional Professor, Applied Mathematics and Computational Sciences

  Jan 27, 12:00 - 13:00

  KAUST

  Backward induction, the cornerstone of dynamic game theory, is the classical algorithm applied to solve finite dynamic games with perfect and complete information. While theoretically sound and beautiful in its simplicity, backward induction does not perform so well when it comes to predicting human behavior. The objective of this seminar is twofold. First, we will understand what backward induction is and how to apply it on game-theoretic trees. Second, we will answer the question of whether backward induction is a good model of how people make choices in dynamic interactions.
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  Elliptic PDEs: Regularity under minimal assumptions - Part 1

  Prof. Giuseppe Di Fazio, Department of Mathematics, University Of Catania, Italy

  Jan 26, 15:00 - 17:00

  B1 L4 R4102

  Elliptic PDEs are ubiquitous both in Mathematics and in applications of Mathematics. Regularity of generalized solutions is a fundamental issue necessary to handle in a proper way if one wants to obtain qualitative information about solutions. My goal is to introduce the audience to the topic of regularity for elliptic PDEs under assumptions on the coefficients that are of minimal requirements.
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  RobotoKAUST 2022 Call for Poster Abstracts

  Prof. Eric Feron, Electrical and Computer Engineering

  Jan 25, 12:00 - Jan 31, 17:00

  KAUST

  RobotoKAUST

  We are excited to announce the KAUST Research Conference on Robotics and Autonomy 2022, which will be held on Feb 28 - March 2, 2022 (#RobotoKAUST). The conference will address the most recent trends of robotics application in a range of disciplines. The conference will give space for presentations and discussions of systems, items, and methods related to robotics and autonomy. As part of this event, we are accepting poster abstract submissions.
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  Extensions of Dynamic Programming for Combinatorial Optimization and Data Mining - 2022-01-24

  Mikhail Moshkov, Professor, Applied Mathematics and Computational Sciences

  Jan 24, 12:00 - 13:00

  KAUST

  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.
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  Towards Compact Grid Interactive EV Charging

  Shehab Ahmed, Professor, Electrical and Computer Engineering

  Jan 23, 12:00 - 13:00

  KAUST

  Towards Compact Grid Interactive EV Charging

  After a quick overview of the ECE Graduate Seminar logistics, I will share the progression of our team’s work in the field of compact integrated on-board EV charging.

Jan 16 - Jan 22, 2022

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  Miniaturized Drug Delivery Systems for Biomedical Applications

  Khalil Moussi, Postdoctoral Research Fellow, Electrical and Computer Engineering

  Jan 18, 18:15 - 20:00

  KAUST

  Highly integrated and customizable systems have been a principal focus of development for parenteral and oral drug administration. Extensive work has been done to optimize drug efficacy via localized delivery and dosage control providing new ways for accomplishing targeted therapeutic effects. However, many challenges and opportunities for advancement remain. One promising research path is introducing novel microfabrication methods or engineering discoveries in concept realization, making devices more versatile and effective.

Jan 9 - Jan 15, 2022

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  Multilevel Methods for Stochastic Forward and Inverse Problems

  Marco Ballesio, Ph.D., Applied Mathematics and Computational Sciences

  Jan 12, 11:00 - 12:00

  KAUST

  This thesis studies novel and efficient computational sampling methods for applications in three types of stochastic inversion problems: seismic waveform inversion, filtering problems, and static parameter estimation.

Dec 19 - Dec 25, 2021

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  Control Design for Alignment Problem in Optical Wireless Communication

  Asem Al Alwan , Ph.D. Student, Electrical and Computer Engineering

  Dec 23, 16:00 - 18:00

  KAUST

  Optical Wireless Communication (OWC) offers many benefits over established radio frequency-based communication links. In particular, high beam directivity generates efficient power usage and high-speed data services. Moreover, due to its ease of deployment, high transmission security, license-free operation, and insensitivity to interference, the OWC link is becoming an attractive solution for the next generation of communication systems.

Dec 12 - Dec 18, 2021

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  Signal Processing and Optimization Techniques for High Accuracy Indoor Localization, Tracking, and Attitude Determination

  Mohammed Hussain AlSharif, Ph.D. Student (former), Electrical and Computer Engineering

  Dec 16, 14:00 - 15:00

  B1 L3 R3119

  Indoor localization Ultrasonic-based Localization Signal processing

  High-accuracy indoor localization and tracking systems are essential for many modern applications and technologies. However, accurate location estimation of moving targets is challenging. This thesis addresses the challenges in indoor localization and tracking systems and proposes several solutions. A novel signal design, which we named Differential Zadoff-Chu, allows us to develop algorithms that accurately estimate the distances of static and moving targets even under random Doppler shifts. The results show that the proposed algorithms outperform the state-of-the-art in terms of both accuracy and complexity.

Dec 5 - Dec 11, 2021

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  Backward Error Analysis for Nonlinear Differential Equations

  David Ketcheson, Professor, Applied Mathematics and Computational Sciences

  Dec 9, 12:00 - 13:00

  KAUST

  Backward error analysis is a tool that allows us to understand the effect of errors without knowing exact solutions.
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  Deconfounding

  Professor Peter Bühlmann, Statistics and Mathematics, ETH Zürich

  Dec 7, 15:30 - 16:30

  B9 L2 H2

  Hidden confounding is a severe problem when interpreting regression or causal parameters, and it may also lead to poor generalization performance for prediction. Adjusting for unobserved confounding is important but challenging when based on observational data only.
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  Statistical Learning: Causal-oriented and Robust

  Professor Peter Bühlmann, Statistics and Mathematics, ETH Zürich

  Dec 7, 12:00 - 13:00

  B9 L2 H2

  Reliable, robust and interpretable machine learning is a big emerging theme in data science and artificial intelligence, complementing the development of pure black box prediction algorithms. Looking through the lens of statistical causality and exploiting a probabilistic invariance property opens up new paths and opportunities for enhanced interpretation, robustness and external validity, with wide-ranging prospects for various applications.
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  Deploying DNNs in the Embedded Space: Challenges and Opportunities

  Christos-Savvas Bouganis, Reader in Intelligent Digital Systems in the Department of Electrical and Electronic Engineering, Imperial College London, UK

  Dec 6, 12:00 - 13:00

  B9 R2322 H1

  The talk will discuss the challenging problem of designing Deep Neural Network systems that achieve high performance under low power envelopes, hindering their deployment in the embedded space.
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  Integrated Sensors: The synergistic combination of materials and microelectronics - 2021

  Khaled Salama, Professor, Electrical and Computer Engineering

  Dec 5, 12:00 - 13:00

  KAUST

  Integrated Sensors

  In this talk we will present the design and implementation of monolithic and hybrid sensors using integrated circuits.

Nov 28 - Dec 4, 2021

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  Particle filter in high dimensions

  Dec 2, 12:00 - 13:00

  KAUST

  Filtering (or data assimilation) is the process of combining noisy observational data with an output of a numerical model to produce an optimal estimate of the evolving state of the system.
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  From Intrusion Tolerance to Trust and Recovery: A Research Overview

  Professor Miguel Correia, Computer Science and Engineering Department, Instituto Superior Técnico, Universidade de Lisboa

  Dec 1, 12:00 - 13:00

  B9 L3 R3223

  The seminar is an overview on my research path and approach. I will start with my initial research on the concept of intrusion tolerance and present some of the results I had at the time. Then, I will show how this initial research had led me to other areas and related approaches: software security, trusted computing, intrusion detection, intrusion recovery, and blockchain interoperability. I will present with some detail very recent research on securely moving data between trusted execution environments (TEEs) in a non-interactive way, a problem that must be solved for digital id crypto wallets starting to appear in Europe and elsewhere.
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  Robust high temperature operation of quantum dot lasers grown on (001) Si for Photonic Integrated Circuit

  Chen Shang, PhD candidate, University of California, Santa Barbara

  Dec 1, 08:00 - 09:00

  KAUST

  Epitaxially grown quantum dot (QD) lasers are emerging as an economical approach to obtain on-chip light sources. Thanks to the three-dimensional confinement of carriers, QDs show greatly improved tolerance to defects and promise other advantages such as low transparency current density, high temperature operation, isolator-free operation, and enhanced four-wave-mixing.
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  DeepReduce: A Sparse-tensor Communication Framework for Distributed Deep Learning

  Panagiotis Kalnis, Professor, Computer Science

  Nov 29, 12:00 - 13:00

  B9 R2322 H1

  Sparse tensors appear frequently in distributed deep learning, either as a direct artifact of the deep neural network's gradients, or as a result of an explicit sparsification process. Most communication primitives are agnostic to the peculiarities of deep learning; consequently, they impose unnecessary communication overhead.
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  Securing 5G and Beyond Networks from Malware Epidemics: A Percolation Theoretic Approach

  Hesham Elsawy, Assistant Professor, Electrical and Computer Engineering KFUPM, Dhahran

  Nov 28, 13:00 - 14:00

  B1 L3 R3119

  5G networks malware

  This talk will introduce and motivate a percolation theoretic approach to characterize and thwart malware diffusion in dense wireless networks. First, a brief tutorial on percolation theory will be introduced. Then, the talk will explain the utilization of percolation theory to develop security countermeasures for malware epidemics in 5G and beyond networks.
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  Exploiting Spatial-Temporal Graph Structure in the Data for Better EEG Source Imaging

  Feng Liu, Assistant Professor, School of Systems and Enterprises at Stevens Institute of Technology

  Nov 28, 12:00 - 13:00

  KAUST

  Exploiting Spatial

  The electroencephalogram (EEG) is an important neuroimaging modality to measure neuronal activity of the human brain.