Events

Mar 26 - Apr 1, 2023

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  Mathematics of Emergent Phenomena in Networks and Collective Motion

  Jan Haskovec, Senior Research Scientist, Applied Mathematics and Computational Sciences

  Mar 26, 12:00 - 13:00

  B9 L3 R3131

  Emergence of nontrivial patterns via collective actions of many individual entities is an ever-present phenomenon in physics, biology and social sciences. It has numerous applications in engineering, for instance, in swarm robotics. I shall demonstrate how tools from mathematical modeling and analysis help us gain understanding of fundamental principles and mechanisms of emergence. I will present my recent results in consensus formation and flocking models, taking into account their realistic aspects - noise, latency, finite speed of information propagation and anticipation. Moreover, I will introduce a continuum modeling framework for biological network formation, where emergence takes place through the interaction of structure and medium. The models are formulated in terms of ordinary, stochastic and partial differential equations. I shall explain how mathematical analysis of the respective models contributes to the understanding of how individual rules generate and influence the patterns observed on the global scale. Finally, I will explain how requirements on robustness of the network can be incorporated into the mathematical model.
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  Thoughts on software and systems engineering - 2023-03-26

  Eric Feron, Professor, Electrical and Computer Engineering

  Mar 26, 12:00 - 13:00

  B9 L2 H2

  Complex systems and software engineering, and associated challenges become increasingly important for the well-being and safety of our society of humans.

Mar 19 - Mar 25, 2023

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  Making federated learning practical and confidential

  Dr. Ruichuan Chen, Distinguished Member of Technical Staff and a Tech Lead, Nokia Bell Labs

  Mar 23, 15:30 - 16:30

  B4 L5 R5209

  Federated learning

  Federated learning (FL) is increasingly deployed among multiple clients to train a shared model over decentralized data. To address the privacy concerns, FL systems need to protect the clients' data from being revealed during training, and also control data leakage through trained models when exposed to untrusted domains. However, existing FL systems (with distributed differential privacy) work impractically in the presence of client dropout, resulting in either poor privacy guarantees or degraded training accuracy. In addition, existing FL systems focus on safeguarding the privacy of training data, but not on protecting the confidentiality of the models being trained, which are increasingly of high business value. In this talk, I will present two pieces of our recent work that aim to address these aforementioned issues.
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  Interpretation, Verification and Privacy Techniques for Improving the Trustworthiness of Neural Networks

  Arnaud Dethise, Ph.D. Student, Computer Science

  Mar 22, 12:30 - 14:30

  B1 L3 R3119

  This presentation addresses the challenges associated with trusting Neural Networks due to their black-box nature and limited ability to answer important questions on how they behave. The thesis proposes techniques that increase the trustworthiness of Neural Network models by employing approaches to overcome their black-box nature. The techniques include efficient extraction and verification of weights and decisions to ensure correctness with regards to pre-existing properties, continuous and exact explanations of the model behavior, and scalable training techniques providing strong, theoretically provable guarantees of privacy. We provide strong, approximation-free guarantees about Neural Networks, improving their trustworthiness to make it more likely that users will be willing to deploy them in the real world.
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  Proximal Algorithms for Large-Scale Convex Non-smooth Optimization

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

  Mar 21, 16:00 - 17:00

  B2 L5 R5220

  algorithms stochastic algorithm

  Convex nonsmooth optimization problems, whose solutions live in very high dimensional spaces, have become ubiquitous. To solve them, the class of iterative fixed-point algorithms known as proximal splitting algorithms is particularly adequate: they consist of simple operations, handling the terms in the objective function separately. I will present a selection of recent primal-dual algorithms within a unified framework, which consists in solving monotone inclusions with well-chosen spaces and metrics.
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  Tackling credential abuse together

  Michael Reiter, James B. Duke Distinguished Professor, Departments of Computer Science and Electrical & Computer Engineering, Duke University

  Mar 20, 12:00 - 13:00

  B9 L2 H2 H2.

  data security

  Despite long-ago predictions (e.g., see Bill Gates, 2004) that other user-authentication technologies would replace passwords, passwords remain not only pervasive but have flourished as the dominant form of account protection, especially at websites such as retailers that require a low-friction user experience. This talk will describe our research on methods to tackle three key ingredients of account takeovers for password-protected accounts today: (i) site database breaches, which is the largest source of stolen passwords for internet sites; (ii) the tendency of users to reuse the same or similar passwords across sites; and (iii) credential stuffing, in which attackers submit breached credentials for one site in login attempts for the same users' accounts at another.
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  On Fourier methods and machine learning techniques in Computational Finance.

  Prof. Kees Oosterlee, Utrecht University

  Mar 20, 12:00 - 13:00

  B9 L3 R3128

  In this presentation we will explain how we can solve linear, semi-linear as well as nonlinear partial differential equations by the concept of backward stochastic differential equations and Fourier cosine expansions. We will discuss the highly efficient pricing of financial options in the Fourier context by means of the COS method. Particularly, we also present a new jump-diffusion process, the Heston-Queue-Hawkes (HQH) model, combining the well-known Heston model and the recently introduced Queue-Hawkes (Q-Hawkes) jump process. Like the Hawkes process, the HQH model can capture the effects of self-excitation and contagion of stock prices.
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  5th KAUST CEMSE MaS Workshop on Modelling and Simulation

  Speakers from KAUST, Melbourne, Utrecht, Karlsruhe, Erlangen, Brisbane, Langensteinbach, Lugano, Frankfurt, Italy

  Mar 20, 09:00 - Mar 25, 17:30

  B3 L5 R5209

  Applied Partial Differential Equations

  The workshop provides a forum for researchers to present and discuss recent progress in modelling and simulation research. Topics are Simulating unsteady CO 2 flow through brine-saturated cross-bedded sandstones: towards relative permeability curves for unstable displacement On Fourier methods and machine learning techniques in Computational Finance Pore-scale modelling of flow and transport in porous media Uncertainty Quantification of Density-Driven Groundwater Flow Simulation of unsaturated Density-Driven flow with a phreatic surface Modeling and Large-scale Simulation of Carbon Dioxide
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  Monolithic Quantum Devices on SOI for Silicon Photonics

  Prof. Kei May Lau, Electronic Engineering, Chinese University, Hong Kong

  Mar 19, 12:00 - 13:00

  B9 L2 H2

  Monolithic Quantum Devices

  Si photonics has been developed to enable the next generation tele- and data-communications for its high performance, making use of the mature silicon CMOS technologies.

Mar 12 - Mar 18, 2023

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  Gravitational Collapse of Self-similar stars

  Dr. Matthew Schrecker, Departments of Mathematics, University College London

  Mar 16, 16:00 - 17:00

  B1 L4 R4102

  Nonlinear Partial Differential Equation Gravitational collapse UCL

  The Euler-Poisson equations give the classical model of a self-gravitating star under Newtonian gravity. It is widely expected that, in certain regimes, initially smooth initial data may give rise to blow-up solutions, corresponding to the collapse of a star under its own gravity. In this talk, I will present recent work with Yan Guo, Mahir Hadzic and Juhi Jang that demonstrates the existence of smooth, radially symmetric, self-similar blow-up solutions for this problem. I will also comment on the stability of the obtained solution. At the heart of the analysis is the presence of a sonic point, a singularity in the self-similar model that poses serious analytical challenges in the search for a smooth solution.
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  Towards Neurosymbolic AI: From Knowledge Infrastructure to Explainable Predictions

  Dr. Michel Dumontier, Distinguished Professor, Data Science

  Mar 16, 12:00 - 13:00

  B2 L5 R5220

  data science

  Abstract The increased availability of biomedical data, particularly in the public domain, offers the opportunity to better understand human health and to develop effective therapeutics for a wide range of unmet medical needs. However, data scientists remain stymied by the fact that data remain hard to find and to productively reuse because data and their metadata i) are wholly inaccessible, ii) are in non-standard or incompatible representations, iii) do not conform to community standards, and iv) have unclear or highly restricted terms and conditions that preclude legitimate reuse. These
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  Neuroimplants for Neurodegenerative Conditions of the Eye and the Brain: Predictive Multiscale Computational Modeling, System Design, and Experimental Testing

  Gianluca Lazzi, PhD MBA is a Provost Professor of Ophthalmology, Electrical Engineering, Clinical Entrepreneurship and Biomedical Engineering at the University of Southern California (USC)

  Mar 16, 10:00 - 11:00

  B9 H1 R2322

  Technical challenges Neuroprosthetics Bioelectromagnetic systems

  Although technical challenges are still daunting, the clinical utility of neuroprosthetics has increased dramatically over the past few years. This has been accomplished through the convergence of numerous disciplines, which have individually added fundamental understanding/capabilities to systems that interface with the human body to restore senses and movement, or treat prevalent diseases that have currently no foreseeable cure. Among these, predictive multiscale computational modeling methods have greatly aided in the design of neuroprosthetics by embracing the complexity of the nervous system, which span multiple spatial scales, temporal scales, and disciplines. In this talk, we will cover some of the recent advances in bioelectromagnetic systems for healthcare, with a particular focus on visual and hippocampal prosthesis, peripheral neuroprosthetics, and sensors.
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  On Inference About the Smoothness Parameter in Gaussian Matern Random Fields

  Prof. Victor DeOliveira, Professor in Department of Management Science and Statistics in the Carlos Alvarez College of Business

  Mar 15, 15:00 - 16:00

  B1 L4 R4102

  The Mat\'ern family of covariance functions is currently the most commonly used for the analysis of geostatistical data due to its ability to describe different smoothness behaviors. Yet, in many applications the smoothness parameter is set at an arbitrary value.
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  Mathematical Analysis of Atmospheric and Oceanic Dynamics Models: Cloud Formation and Sea-ice Models

  Prof. Edriss S. Titi

  Mar 14, 16:00 - 17:00

  B3 L5 R5209

  atmosphere

  In this talk we will present rigorous analytical results concerning global regularity, in the viscous case, and finite-time singularity, in the inviscid case, for oceanic and atmospheric dynamics models. Moreover, we will also provide a rigorous justification of the derivation of the Primitive Equations of planetary scale oceanic dynamics from the three-dimensional Navier-Stokes equations as the vanishing limit of the small aspect ratio of the depth to horizontal width.
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  Algebraic Structures from beginning to Lie algebras-Part 2

  Associate Professor Amir Baklouti, Department of Mathematics, Umm Al-Qura University (KSA

  Mar 14, 14:00 - 16:00

  B1 L4 R4102

  This brief course will cover several algebraic structures, beginning with groups and culminating with algebras. Our primary the focus will be on Lie algebras, and we will introduce the essential properties and results necessary to comprehend the structure of Lie algebras and other related structures
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  Robust Geometry Processing for Physical Simulation

  Prof.Daniele Panozzo

  Mar 13, 15:45 - 16:45

  B4 L5 R5209

  geometry processing physical simulation

  I will discuss the limitations of the current state of the art, and present a proposal for an integrated pipeline, considering data acquisition, meshing, basis design, and numerical optimization as a single challenge, where tradeoffs can be made between different phases to increase automation and efficiency. I will demonstrate that this integrated approach offers many advantages, while opening exciting new geometry processing challenges, and that a fully opaque meshing and analysis solution is already possible for heat transfer and elasticity problems with contact. I will present a set of applications enabled by this approach in reinforcement learning for robotics, force measurements in biology, shape design in mechanical engineering, stress estimation in biomechanics, and simulation of deformable objects in graphics.
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  Inter-Surface Maps

  Prof.Leif Kobbelt

  Mar 13, 14:30 - 15:30

  B4 L5 R5209

  inter-surface maps 3D shapes

  Many tasks in the analysis and synthesis of (collections of) 3D shapes boils down to computing a map between two surfaces. Such inter-surface maps can be used to establish correspondences, to transfer information or annotation from one object to another, or to plausibly deform one shape into another. If the two shapes are represented as polygon meshes, a continuous inter-surface map does not only assign the vertices of the source mesh to the target mesh but also maps the interior of the triangles which adds to the complexity of the task.
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  Discrete Surfaces from Developable Strips

  Prof.Christian Müller

  Mar 13, 13:30 - 14:30

  B4 L5 R5209

  Geometry geometry processing developable strips

  We will discuss several settings in which smooth developable strips are attached to each other or assembled in some other way to obtain surfaces with interesting geometric properties. Driven by the view towards applications we will investigate collections of strips which lie orthogonal or tangential to a reference surface and assume particular shapes. Such strips can serve as support structures or cladding panels of free-form shapes in architectural contexts. Our focus will lie on surfaces with a constant ratio of principal curvatures, cone nets, geodesic grid shells, and others.
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  Decision Trees for Fault Diagnosis in Circuits and Switching Networks - 2023-03-13

  Mikhail Moshkov, Professor, Applied Mathematics and Computational Sciences

  Mar 13, 12:00 - 13:00

  B9 L3 R3128

  We study theoretical problems of fault diagnosis in circuits and switching networks, which are among the most fundamental models for computing Boolean functions. We investigate two main cases: when the scheme (circuit or switching network) has the same mode of operation for both calculation and diagnostics, and when the scheme has two modes of operation -normal for calculation and special for diagnostics.
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  Computing in the Specialization Era

  Prof.Gustavo Alonso, Computer Science, ETH Zurich

  Mar 13, 12:00 - 13:00

  B9 L2 H2 H2

  cloud computing FPGA Hardware acceleration

  In this talk I will discuss the shift towards hardware acceleration and show with several examples from industry and from research the large role that FPGAs are playing. I will hypothesize that we are in a new era where most of the established assumptions, rules of thumb, and accumulated wisdom about many aspects of computation in general and of data processing in particular no longer hold and need to be revisited.
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  Cohomology in fluids

  Prof.Albert Chern

  Mar 13, 11:15 - 12:15

  B4 L5 R5209

  fluids cohomology fluid dynamics Geometry geometry processing

  We present a topological analysis of the vorticity formulation in describing fluid dynamics. Despite its widespread use in fluid mechanics, this formulation is insufficient at describing fluid dynamics on a non-simply-connected domain. What is missing is an equation of motion for fluid's cohomology component, which exhibits fascinating dynamics previously under explored. Using geometric language, we derive the new equation of motion and establish new conservation laws, as Casimir invariants in Hamiltonian mechanics, for fluids on domains with general topology. Significantly, we present the first physically correct vortex method on curved surfaces with genus and boundaries.
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  Discrete Laplacians for Polygons and Polyhedral

  Prof.Mario Botsch

  Mar 13, 10:15 - 11:15

  B4 L5 R5209

  discrete laplacians polygons polyhedra computer graphics

  Being able to accurately solve PDEs on arbitrary polygonal/polyhedral meshes is a central goal and has been considered for various differential operators over the last years. In this talk I will present a simple approach for computing (piecewise) linear and quadratic basis functions for general polygons and polyhedra, from which discrete operators for gradient, divergence and Laplacian can be derived.
• 

  Algebraic Structures from beginning to Lie algebras- Part 1

  Associate Professor Amir Baklouti, Department of Mathematics, Umm Al-Qura University (KSA

  Mar 13, 10:00 - 12:00

  B1 L4 R4102

  This brief course will cover several algebraic structures, beginning with groups and culminating with algebras. Our primary the focus will be on Lie algebras, and we will introduce the essential properties and results necessary to comprehend the structure of Lie algebras and other related structures.
• 

  5-axis flank CNC machining of free-form surfaces using custom-shaped tools

  Dr.Michael Barton

  Mar 13, 09:00 - 10:00

  B4 L5 R5209

  free-form surfaces geometric computing

  In this talk, I will discuss our recent advances in approximation of free-form surfaces by motions of curvature varying tools in the context of 5-axis flank CNC machining. In particular, I will discuss path-planning strategies using fixed tools, or custom-shaped ones, and on an example of spiral bevel gears will demonstrate even more efficient variant of flank machining, called double-flank.
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  Workshop on Applied Geometry and Visual Computing

  Mar 13, 08:55 - 17:00

  B4 L5 R5209

  applied geometry visual computing discrete differential geometry geometry processing

  The “KAUST Workshop on Applied Geometry and Visual Computing” brings together leading scientists from Europe and the United States, presenting their latest results in - Applied and Discrete Differential Geometry - Geometry Processing - Computational Fabrication The talks are related to various problems in Applied Mathematics in general and to further areas of Visual Computing such as Computer Graphics, Physical Simulation and Scientific Visualization. The workshop provides a great opportunity to learn about latest developments and to discuss ongoing work with top researchers in the field.
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  Nanoparticles for Safe and Efficient Delivery of Nucleic Acids and CRISPR Genome Editors

  Shaoqin Sarah Gong, Ophthalmology and Visual Sciences and Biomedical Engineering, University of Wisconsin–Madison

  Mar 12, 12:00 - 13:00

  B9 L2 H2

  Gene therapy, particularly, genome editing therapy can potentially be used to treat many genetic diseases including some currently without a cure.

Mar 5 - Mar 11, 2023

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  An Overview of Systems Research at TU Munich

  Pramod Bhatotia, Chair Professor, Department of Computer Science, the Technical University of Munich (TUM), Germany.

  Mar 9, 15:30 - 16:30

  B4 L5 R5209

  distributed systems operating systems

  In this talk, Professor Pramod Bhatotia will give an overview of systems research at TU Munich. He will cover his teaching and ongoing research projects. And will conclude the talk with a brief overview of his ERC project.
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  Functional Differential Equations in Models of Collective Behavior - Colloquium

  Jan Haskovec, Senior Research Scientist, Applied Mathematics and Computational Sciences

  Mar 7, 16:00 - 17:00

  B4 L5 R5220

  Functional Differential Equation Models collective behavioral analysis

  I will give an overview of recent results for models of collective behavior governed by functional differential equations. The talk will focus on models of interacting agents with applications in biology (flocking, swarming), social sciences (opinion formation) and engineering (swarm robotics), where latency (delay) plays a significant role. I will explain that there are two main sources of delay - inter-agent communications and information processing
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  Machine Learning for Scalable MIMO Communication Systems: From Theory to Real-World Demonstration

  Dr. Ahmed Alkhateeb, Electrical Engineering , Arizona State University

  Mar 6, 14:00 - 15:00

  B9 L2 H1

  communication systems machine learning

  Large-scale MIMO communication (at sub-6GHz, mmWave, and sub-THz bands) is a key enabler for 5G, 6G, and beyond. Scaling MIMO systems, however, is subject to critical challenges, such as the large channel acquisition and beam training overhead and the sensitivity to channel estimation errors (especially at lower frequencies) and blockages (at higher frequencies). These challenges make it difficult for MIMO systems to support applications that have high mobility and strict reliability constraints. In this talk, I will first motivate the use of machine learning and sensory data to address these challenges. Then, I will present a few key machine-learning roles, enabling datasets, and recent hardware proof-of-concept prototypes that demonstrate the machine-learning gains in real-world environments.
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  Bayesian Deep Learning

  Maurizio Filippone, Associate Professor, EURECOM, France

  Mar 6, 12:00 - 13:00

  B9 L3 R3128

  Bayesian computational statistics Deep learning

  The impressive success of Deep Learning (DL) in predictive performance tasks has fueled the hopes that this can help addressing societal challenges by supporting sound decision making. However, many open questions remain about their suitability to hold up to this promise. In this talk, I will discuss some of the current limitations of DL, which directly affect their wide adoption. I will focus in particular on the poor ability of DL models to quantify uncertainty in predictions, and I will present Bayesian DL as an attractive approach combining the flexibility of DL with probabilistic reasoning. I will then discuss the challenges associated with carrying out inference and specifying sensible priors for DL models. After presenting a few of my contributions to address these problems, I will conclude by presenting some interesting emerging research trends and open problems which define my current research agenda.
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  A Data Science Pipeline for Big Linked Earth Observation Data

  Prof.Manolis Koubarakis, Informatics and Telecommunications, National and Kapodistrian University of Athens

  Mar 6, 12:00 - 13:00

  B9 L2 H2 H2

  pipeline Geospatial Data GeoSPARQL

  I will present a data science pipeline which starts with Earth observation data arriving in the ground segment of a satellite mission and ends with a complete user application. I will first briefly present all the tools my group has been developing since 2010 for supporting the various stages of the pipeline. Then, I will concentrate on the recently developed system Strabo 2 which can store big geospatial data encoded in RDF and query them using the Open Geospatial Consortium standard GeoSPARQL. Strabo 2 is the only parallel and distributed RDF store available today that can manage terabytes of geospatial data efficiently.
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  Dynamic Stability Challenges in the European Electric Power Grid

  Rafael Segundo and Petr Korba, Electrical Engineering, Zurich University of Applied Sciences, Switzerland

  Mar 5, 12:00 - 13:00

  B9 L2 H2 H2

  The pressure to achieve carbon-free grids in the near future is driving the rapid integration of renewable energy sources into the grid.

Feb 26 - Mar 4, 2023

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  A novel structure preserving scheme for Schrondiger-Poisson systems.

  Dr. Th. Katsaounis

  Feb 28, 16:00 - 17:00

  B2 L5 R5220

  Novel structure preserving Schrondiger Poisson Systems

  In this talk I will present a new structure preserving, second order in time relaxation-type scheme for approximating solutions of the Schrodinger-Poisson system. We use the Crank-Nicolson scheme as a time-stepping mechanism, whilst the nonlinearity is handled by means of a relaxation approach. For the spatial discretisation we use the standard conforming finite element scheme. The resulting scheme is explicit with respect to the nonlinearity, i.e. it requires the solution of a linear system for each time-step, and satisfies discrete versions of the system’s mass conservation and energy balance laws for constant discretization parameters.
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  Extremes of stationary time-series - 2023-02-09

  Prof. Ioannis Papastathopoulos, Lecturer in Statistics, University of Edinburgh

  Feb 28, 09:00 - 16:00

  B1 L4 R4102

  Refined characterizations of the probabilistic behavior of a stationary time-series by focusing on re normalized Markov processes that are conditioned to attain an extreme event, subject to the level of the extremity tending to the upper end point of the marginal distribution
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  Controlling acoustic wave propagation via rotational motion

  Ying Wu, Professor, Applied Mathematics and Computational Sciences

  Feb 27, 12:00 - 13:00

  B9 L2 H2 H2

  acoustic waves

  Propagation of acoustic waves in time-varying and/or moving media has attracted a lot of attentions and is expected to lead to many intriguing applications. In this talk, I will discuss our recent work on acoustic wave propagation in spinning media (air or water). I will start with a review of the theoretical foundation built upon the Mie scattering framework, in which both the wave equation and the boundary conditions will be specifically discussed. The study is limited in the linear regime and exhibit the peculiar scattering features.
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  Extremes of stationary time-series - 2023-02-27

  Prof. Ioannis Papastathopoulos, Lecturer in Statistics, University of Edinburgh

  Feb 27, 09:00 - 16:00

  B1 L4 R4102

  Theoretical and practical aspects associated with the limiting distributions of block-maxima and peaks-over-threshold events in the case of stationary time-series data. Special emphasis will be placed on the extremal index, a key measure of extremal dependence that allows us to quantify the degree of clustering at the tail of a time-series.
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  Learned Optics — Improving Computational Imaging Systems through Deep Learning and Optimization

  Wolfgang Heidrich, Professor, Computer Science

  Feb 26, 12:00 - 13:00

  B9 L2 H2

  Learned Optics

  Computational imaging systems are based on the joint design of optics and associated image reconstruction algorithms.

Feb 19 - Feb 25, 2023

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  Visualization Methods for Graphs&Networks

  Prof.Oliver Deussen, Visual Computing, University of Konstanz

  Feb 20, 12:00 - 13:00

  B9 L2 H2 H2

  visualization force-directed layouts stress-based methods vector-based constraints

  Inevitably, the projection of most graph structures on two-dimensional screens will create errors and therefore visually wrong impressions. In the past, two types of methods have been developed to minimize projection errors and distribute them in a visually pleasing way. The first group of methods, force-directed layouts, interpret the links of a graph as physical springs, while stress-based methods minimize an energy function, which aims to map graph distances faithfully.
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  Development of Visible VCSELs at Sony

  Tatsushi Hamaguchi, Electronic Engineering, Sony Corporation

  Feb 19, 12:00 - 13:00

  B9 L2 H2

  Development of Visible VCSELs

  Visible VCSELs have been attracting researchers' interest for potential applications as light sources for optical storage, laser printers, projectors, displays, solid-state lighting, optical communications, biosensors, and so on.

Feb 12 - Feb 18, 2023

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  Generative Models for Neural Fields

  Ivan Skorokhodov, Ph.D. Student, Computer Science

  Feb 15, 20:10 - 22:00

  B1 L2 R2202

  Deep generative models

  In computer vision, generative AI models are typically built for images, videos, and 3D objects. Recently, there has emerged a paradigm of neural fields, which unifies the representations of such types of data by parametrizing them via neural networks. In this thesis, we develop generative models for images, videos, and 3D scenes which treat the underlying data in such a form and explore the benefits which such a perspective provides.
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  Extracting Semantic and Geometric Information in Images and Videos using GANs

  Rameen Abdal, Ph.D. Student, Computer Science

  Feb 15, 18:00 - 20:00

  B1 L2 R2202

  GaN

  The success of Generative Adversarial Networks (GANs) has resulted in unprecedented quality both for image generation and manipulation. Recent state-of-the-art GANs (e.g., the StyleGAN series) have demonstrated outstanding results in photo-realistic image generation. In this dissertation, we explore the latent space properties, including image manipulation, extraction of 3D properties, and performing various weakly supervised and unsupervised downstream tasks using StyleGAN and its derivative architectures.
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  Electromagnetic Analysis of Plasmonic Nanostructures by Using a Coupled system Volume Integral and Hydrodynamic Equations

  Doolos Aibek Uulu, Ph.D. Student, Electrical and Computer Engineering

  Feb 15, 14:30 - 16:30

  B2 L5 R5220

  Plasmonic Nanostructures Electromagnetic Analysis

  In this dissertation, three different coupled systems of volume integral and hydrodynamic equations are formulated to analyze electromagnetic scattering from composite plasmonic structures consisting of dielectrics, metals, or semiconductors.
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  Nanostructured Group-III Nitrides for Photoelectrocatalytic Applications and Renewable Energy Harvesting

  Huafan Zhang, Ph.D. Student, Electrical and Computer Engineering

  Feb 14, 16:00 - 18:00

  B4 L5 R5209

  Photoelectrocatalytic Applications renewable energy

  Leveraging on the unique solar light absorption behavior, chemical stability, and piezoelectric properties, nanostructured group-III-nitrides were utilized for implementing various renewable energy harvesting and conversion applications. By using different dimensional nanostructures, especially nanowires, and nanoporous membranes, this dissertation explored the practical applications in photocatalytic nitrogen reduction reaction, photocatalytic wastewater remediation, and flexible self-powered piezoelectric sensing.
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  Asymptotic Analysis and Numerical Methods for Compressible Flows at Low Mach Numbers

  Prof. Dr. Andreas Meister, Professor, Department of Mathematics and Natural Sciences, University of Kassel

  Feb 14, 16:00 - 17:00

  B1 L4 R4102

  Asymptotic Performance Analysis numerical methods compressible flows Mach Numbers

  We will present a comprehensive study of a finite volume method for inviscid and viscous flow fields at high and low speeds. Thereby, the results of a formal asymptotic low Mach number analysis are used to extend the validity of the numerical method from the simulation of compressible flow fields at transonic as well as supersonic speed to the low Mach number regime.
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  An introduction to Patankar-type schemes

  Thomas Izgin, M.Sc., Department of Mathematics, University of Kassel

  Feb 14, 14:00 - 15:00

  B9 L3 R3229

  numerical analysis

  We present order conditions for various Patankar-type schemes as well as a new stability approach that examines the non-hyperbolic fixed points of the schemes for a general linear test problem. We formulate sufficient conditions for the stability of such non-hyperbolic fixed points and the local convergence of the numerical approximation towards the correct steady-state solution of the underlying test problem. To illustrate the theoretical results, we consider several members of the modified Patankar-type family within numerical experiments.
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  Research on Sound and Vibration @ Hong Kong PolyU

  Prof Li Cheng, Chair Professor and Director, The Hong Kong Polytechnic University

  Feb 13, 12:00 - 13:00

  B9 L3 R3128

  acoustic-elastic metamaterials

  As one of the founding departments in the Hong Kong Polytechnic University (PolyU) eighty-five years ago, the Department of Mechanical Engineering (ME) has been actively involved in both fundamental and applied research to advance the frontier of knowledge to serve Hong Kong, its surrounding region and beyond. The research foci of the department are in four major areas under three Research Centres and a Research Consortium under the University Niche Area scheme.
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  Entrepreneurship Explored

  Prof.Bülent Erbilgin and Dr.Lama Hakem, KAUST Entrepreneurship Center

  Feb 13, 12:00 - 13:00

  B9 L2 H2 H2

  entrepreneurship Market marketing

  Entrepreneurs continue to be the driver for economic development and innovation. Some startups invent brand new markets while other manage to enter markets crowded by existing large companies. In this seminar we will explore making critical early decisions starting from chaos and creating an exciting new business. We will gain insights on the value of learning by doing, prototyping, discussing tradeoffs between analysis, experimentation and scale. We will also review courses offered by KAUST Entrepreneurship Center.
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  Conditional Kernel Density Estimation For Dependent Data, Mean Shift Algorithm and Predictive Regions in Human Health

  Prof. Mohamed El Machkouri, Associate professor, Applied Mathematics, University of Rouen Normandy

  Feb 12, 16:00 - 17:30

  B1 L4 R4102

  nonparametric estimation

  In this talk, we present theoretical asymptotic results for the nonparametric estimation of the conditional density of a scalar response variable Y given the explanatory X taking values in a finite-dimensional space when the sample of observations is considered as a sequence of dependent random variables.
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  Security evaluation of modern industrial control systems

  Michail Maniatakos, Electrical and Computer Engineering, New York University (NYU), Abu Dhabi

  Feb 12, 12:00 - 13:00

  B9 L2 H2

  Security Evaluation

  Recent years have been pivotal in the field of Industrial Control Systems (ICS) security, with a large number of high-profile attacks exposing the lack of a design-for-security initiative in ICS.
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  Designing, Synthesizing, and Automating Domain-Specific Accelerators

  Jian Weng, PhD student, Computer Science Department, UCLA

  Feb 12, 08:30 - 09:30

  KAUST

  Digital signal processing

  In this talk, I will first discuss how my research democratizes the accelerator designs and unifies the hardware/software innovations by automating the accelerator design process under a unified programming paradigm. By taking advantage of the compiler’s awareness of the program behaviors that profit from hardware specialization, accelerators can be automatically synthesized by searching through a well-defined design space. These automatically designed accelerators achieve comparable cost/performance efficiency compared with prior handcrafted designs. In the rest of the talk, I will also cover how this work inspires me to develop techniques to accelerate emerging application domains by orders-of-magnitude speedup, including digital signal processing and DNN inference, and how I take advantage of this work to revolutionize the FPGA programming paradigm.