Events

Apr 30 - May 6, 2023

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  Robust and Energy-Efficient Embedded Computing

  Semeen Rehman, Assistant Professor, Electrical Engineering and Information Technology, TU Wien

  May 1, 12:00 - 13:00

  B9 L1 R3131

  Embedded systems are currently an indispensable part of our lives because of their pervasive deployment in a wide range of critical applications (e.g., automotive, encryption, and healthcare, etc.) as well as non-critical applications (e.g., image/video processing, etc.). These embedded system form the fundamental components of today’s Cyber Physical Systems (CPS) and Internet-of-Things (IoT), which are subjected to stringent constraints in terms of reliability, security, and power-/energy especially in case of battery-driven scenarios. Due to the shrinking transistor dimensions, embedded computing hardware are increasingly susceptible to a wide range of reliability threats e.g., transient faults (such as soft errors due to high-energy particle strikes) and permanent faults due to design-time process variations and run-time aging effects. These threats may lead to functional and timing errors that may jeopardize the correct application’s executions. Furthermore, security has also become a crucial aspect in today’s systems because of several security threats, e.g., confidentiality threat to steal the IP or private information via side channel attacks. These reliability and security threats may lead to a catastrophic impact on the robustness of embedded systems. Another key design constraint of a battery-driven embedded platform is power-/energy-wise efficiency, e.g., a device under a low power/battery mode during the critical application execution may miss a critical event that may lead to a catastrophic outcome. In order to address the above-mentioned challenges, it is crucial to investigate different techniques at the hardware and software layers. In this talk, I will first highlight the key robustness and energy efficiency challenges in embedded computing systems, and will present techniques to address these challenges.
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  KAUST Research Conference 2023 on Computational Advances in Structural Biology

  Xin Gao, Program Chair, Computer Science

  May 1, 08:00 - May 3, 17:00

  B4 B5 A0215

  structural biology

  Computational Bioscience Research Center (CBRC) is pleased to announce the KAUST Research Conference 2023 on Computational Advances in Structural Biology. The conference will be held from May 1-3, 2023 at the Auditorium between Building 4 & 5. The visualization of the structure of a molecule, organelle or larger entity is key to understanding its biological function. Thus, structural biology is an extremely powerful means of unraveling the fundaments of life, but also of diseases. Consequently, structural biology is at the heart of medical therapies, including drug design, and has enabled the
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  Multimodal Video Analysis

  Juan Carlos L. Alcazar, Postdoctoral Research Fellow, Image and Video Understanding Lab

  Apr 30, 12:00 - 13:00

  B9 L2 H2

  Multimodal Video Analysis

  The recognition of human behavior has driven most advances in video analysis over the past decade.

Apr 16 - Apr 22, 2023

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  CT-based COVID-19 Diagnosis and Prognosis through Novel AI Model Development

  Xin Gao, Program Chair, Computer Science

  Apr 18, 16:00 - 17:00

  B2 L5 R5220

  CT-based COVID-19 Diagnosis Prognosis novel methodology model development

  During the recent pandemic, urgent advances have been made by the scientific community in developing artificial intelligence (AI)-based computer-aided systems for CT-based COVID-19 diagnosis. In this talk, I will introduce our work on a fully-automatic, rapid, accurate, and machine-agnostic method that can segment and quantify the infection regions on CT scans from different sources.
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  Latent Space Manipulation of GANs for Seamless Image Compositing

  Anna Fruehstueck, Ph.D., Computer Science

  Apr 17, 17:30 - 18:30

  B5 L5 R5220

  Generative Adversarial Networks image synthesis texture synthesis

  Generative Adversarial Networks (GANs) are a very successful method for high-quality image synthesis and are a powerful tool to generate realistic images by learning their visual properties from a dataset of exemplars. However, the controllability of the generator output still poses many challenges. In this thesis, we propose several methods for achieving larger and/or higher visual quality in GAN outputs by combining latent space manipulations with image compositing operations
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  Pore-scale modeling of flow and transport in deformable porous media

  S. Majid Hassanizadeh, Professor, Stuttgart University, Germany, Utrecht University, The Netherlands,

  Apr 17, 12:00 - 13:00

  B9 L3 R3128

  Pore-scale models are valuable tools for the investigation of various flow and transport processes in porous media, and upscaling from pore to core scales. Popular approaches include pore-network models (PNM), Lattice-Boltzman models (LBM), Smooth Particle Hydrodynamics (SPH), volume-of-fluid method (VOF), and grain-scale models (GSM).
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  Serverless Computing: With Great Freedom Comes Great Opportunity!

  Marco Canini, Professor, Computer Science

  Apr 17, 12:00 - 13:00

  B9 L2 H2 H2

  serverless computing

  This talk introduces serverless computing, a programming model that has flourished in the last few years, mainly because it allows developers to concentrate on the application logic and not worry about scalability and resource management. Current serverless offerings give users limited flexibility for configuring the resources allocated to their function invocations. We take a principled approach to the problem of resource allocation for serverless functions, analyzing the effects of automating this choice in a way that leads to the best combination of performance and cost.
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  Smart Non-volatile Memory Devices that can Sense and Compute

  Dayanand Kumar, Postdoctoral Research Fellow, Electrical and Computer Engineering

  Apr 16, 12:00 - 13:00

  B9 L2 H2

  Smart non volatile memory devices

  Being renowned for operating with visible light pulses and electrical signals, optoelectronic smart non-volatile memory devices have excellent potential for neuromorphic computing systems and artificial visual information processing.

Apr 9 - Apr 15, 2023

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  Reversal in the Stationary Prandtl Equations

  Prof. Nader Masmoudi

  Apr 13, 13:30 - 14:30

  B2 L5 R5220

  Reversal Stationary Prandtl Equations

  We investigate reversal and recirculation for the stationary Prandtl equations. Reversal describes the solution after the Goldstein singularity, and is characterized by regions in which $u > 0$ and $u 0$. The classical point of view of regarding the Prandtl equations as an evolution equation in $x$ completely breaks down since $u$ changes sign. Instead, we view the problem as a quasilinear, mixed-type, free-boundary problem. Joint work with Sameer Iyer.
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  A personal path from mathematics to biomedical research

  Apr 12, 12:00 - 13:00

  B2 L5 R5209

  Biomedical Computational biology

  A personal presentation describing the role of computational biology and bioinformatics in addressing state-of-the-art biomedical challenges. First, I will provide an overview of the transition between a Ph.D. in Mathematics to a postdoc in Computational Biology: How did it happen? What were the challenges? Secondly, I will briefly present several current case studies where computational biology (in several flavors) is core to understanding novel biological data related to multi-omic data analysis, spatial profiling, gene therapy, and more.
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  High-order non-conforming finite elements for the Stokes equation

  Stefan Sauter, Professor, Institute of Mathematics, University of Zurich

  Apr 11, 16:00 - 17:00

  B2 L5 R5220

  High-order non-confirming Finite elements stokes equation

  We consider non-conforming discretizations of the stationary Stokes equation in two and three dimensions by Crouzeix-Raviart type elements. The original definition in the seminal paper by M. Crouzeix and P.-A. Raviart in 1973 is implicit and also contains substantial freedom for a concrete choice.
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  Towards Designing Robust Deep Learning Models for 3D Understanding

  Abdullah Hamdi, Ph.D., Electrical and Computer Engineering

  Apr 10, 17:00 - 19:00

  B3 L5 R5220

  deep neural networks

  Deep Neural Networks (DNNs) have shown huge success over the years to solve many 2D computer vision tasks driven by massive labeled 2D datasets and advancements in 2D vision models, but less success is witnessed on 3D vision tasks. This dissertation proposes innovative approaches to enhance the robustness of DNNs for 3D understanding and in 3D settings. The research focuses on two main areas: adversarial robustness on 3D data and setups, and the robustness of DNNs to realistic 3D scenarios. Two paradigms for 3D understanding are discussed: representing 3D as a set of 3D points and performing 2D processing of multiple images of the 3D data.
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  Web-based 3D Visualization

  Ivan Viola, Professor, Computer Science

  Apr 10, 12:00 - 13:00

  B9 L2 H2 H2

  nanovisualization computer graphics 3D graphics WebGPU WebAssmbly

  In this seminar I will present how to create 3D computer graphics and visualization systems for the web, using WebAssmbly and WebGPU language specifications, which are new, bleeding-edge technologies. Previously, accelerated graphics on the web was based on JavaScript libraries, which is still very popular, but they do not offer detailed memory management and code optimization, necessary for systems requiring high memory load and high computational demands. WebAssembly and WebGPU can be compiled from the C++ or Rust code, which also allows the deployment of the same codebase either for web or for the desktop-based applications.
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  Robust and efficient algorithms for phase equilibrium calculations of reservoir fluids at constant moles, volume and temperature

  Shuyu Sun, Professor of Earth Science and Engineering، KAUST

  Apr 10, 12:00 - 13:00

  B9 L3 R3128

  Reservoir simulation usually involves the fluid flow of partially miscible multi-component multi-phase mixture in porous media. Phase behavior of fluid mixture is a crucial component of many multi-phase flow framework. Accurate modeling and robust computation of the phase behavior is essential for optimal design and cost-effective operations in petroleum reservoirs as well as in a petroleum processing plant. A typical problem formulation in phase behavior is two-phase constant volume flash, i.e., the two-phase phase-split under the constant temperature, moles, and volume.
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  Automated and Scalable Algorithms and Systems for Unsupervised ML

  Yue Zhao, Computer Engineering, Carnegie Mellon University

  Apr 9, 12:00 - 13:00

  B9 L2 H2

  Automated and scalable algorithms

  Many real-world events do not have outcome labels. For example, the fraudulence of a transaction remains unknown until it is discovered.

Apr 2 - Apr 8, 2023

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  Tides: Introduction and Modelling - Part 3

  Prof. Clodoaldo Grotta Ragazzo, Institute of Mathematics and Statistics of the University of Sao Paulo

  Apr 5, 10:00 - 12:00

  B1 L4 R4102

  "Force-deformation laws, Love numbers, and the Association Principle". This lecture contains a discussion about realistic force-deformation relations used for celestial bodies, their empirical description by means of Love numbers, and their mathematical modelling by means of the "Association Principle''.
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  Modeling and Inference for Multivariate Time Series, with Applications to Integer-Valued Processes and Nonstationary Extreme Data

  Matheus B. Guerrero, Ph.D. Student, Statistics

  Apr 4, 16:00 - 19:00

  B4 L5 R5220

  statistical methods integer-valued data autoregressive processes multivariate nonstationary extreme data

  This Ph.D. research focuses on proposing new statistical methods for two types of time series data: integer-valued data and multivariate nonstationary extreme data. For the former, the researcher proposes a novel approach to building an integer-valued autoregressive (INAR) model that offers the flexibility to specify both marginal and innovation distributions, leading to several new INAR processes. For the latter, the researcher proposes new extreme value theory methods for analyzing multivariate nonstationary extreme data, specifically EEG recordings from patients with epilepsy. Two extreme-value methods, Conex-Connect and Club Exco, are proposed to study alterations in the brain network during extreme events such as epileptic seizures.
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  Numerical Methods for Energy Minimisation: Application to Machine Learning Algorithms

  Prof. Charalambos Makridakis, Director of the Institute of Applied and Computational Mathematics of FORTH, Greece

  Apr 4, 16:00 - 17:00

  B2 L5 R5220

  numerical methods energy minimization Applied Machine Learning

  In this talk, we discuss problems and numerical methods arising in the calculus of variations and energy minimization. Among numerous applications, energy minimization is a core element of Machine Learning algorithms. Within the field of nonlinear PDEs, the calculus of variations has received a lot of attention from the analysis point of view. Although quite interesting and challenging, the numerical analysis of these problems is much less developed.
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  Intelligent Health Monitoring in the Home

  Yuan Yuan, Postdoc Fellow, MIT

  Apr 4, 15:00 - 16:00

  KAUST

  Delivering healthcare to patients in their homes is shaping the future of healthcare, as it offers better access to healthcare for people who live far from hospitals. It also facilitates the early detection of diseases and the prevention of complications that might otherwise require hospitalization, while simultaneously reducing costs for patients and healthcare systems. Nevertheless, the utilization of machine learning and health sensors for clinical purposes within the home environment requires addressing certain challenges, such as simplifying compliance procedures, ensuring that the digital representations of patients are comparable to the established medical gold standard, and preserving data privacy. In this talk, I will discuss how to develop new algorithms and models with health sensors to capture rich, continuous representations for in-home healthcare applications that address all these challenges.
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  Tides: Introduction and Modelling - Part 2

  Prof. Clodoaldo Grotta Ragazzo, Institute of Mathematics and Statistics of the University of Sao Paulo

  Apr 4, 10:00 - 12:00

  B1 L4 R4102

  "Translation, rotation and Deformation". In this lecturer, we will present the simplest possible model for the motion of two extended rigid bodies interacting by gravity.
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  Using the smithy of computational science to enhance the robustness and efficiency of complex fluid flow simulations.

  Matteo Parsani, Associate Professor, Applied Mathematics and Computational Science

  Apr 3, 12:00 - 13:00

  B9 L3 R3128

  This talk will review the recent shift in the construction and modern analysis of a large class of spatially and temporally adaptive methods whose properties are very close to our current analytical knowledge about hyperbolic or mixed hyperbolic-parabolic partial differential equations (PDEs) in computational physics. Thus these algorithms can be regarded as the elite methods in the field. Next, we will show examples of how the robustness and efficiency of the fully-discrete representation of PDEs can be enhanced using computational science's smithy, i.e., "modern" numerical analysis. The talk will showcase complex flow problems in aeronautics, aerospace, and automotive sectors, provide preliminary results in other fields, and present an outlook for future research directions where data science can currently be the linesman.
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  Cybersecurity of Industrial Control Systems - Challenges and Research Directions

  Awais Rashid, Professor of Cybersecurity, the University of Bristol, Director of the EPSRC Centre for Doctoral Training in Trust, Identity, Privacy and Security in Large-Scale Infrastructures

  Apr 3, 12:00 - 13:00

  B9 L2 H2 H2.

  cybersecurity industrial control systems

  This Distinguished Lecture is part of the CS Graduate Seminars. Abstract Industrial Control Systems play an important role in monitoring, controlling, and automating critical infrastructure such as water, gas, oil, and electricity. Recent years have seen a number of high-profile cyber attacks on such infrastructure, exemplified by Triton, Colonial Pipeline, and the Ukrainian Power Grid attacks. This naturally begs the question: how should we manage cyber security risks in such infrastructure on which the day-to-day functioning of our society relies? What are the complexities of managing
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  Tides: Introduction and Modelling - Part 1

  Prof. Clodoaldo Grotta Ragazzo, Institute of Mathematics and Statistics of the University of Sao Paulo

  Apr 3, 10:00 - 12:00

  B1 L4 R4102

  "Qualitative theory of tides and their effects." It contains a qualitative explanation of tides and their effects: phase lags, forces that dissipate energy but do not dissipat angular momentum, circularization of orbits, spin-orbit synchronization and collision.
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  Non-Abelian Braiding with Sound and Light

  Guancong Ma, Associate Professor, Department of Physics, Hong Kong Baptist University

  Apr 3, 09:30 - 10:30

  B9 L2 R2131

  Abstract Non-Abelian-based theories are one of the foundations of modern physics. For example, non-Abelian braiding realized by the dynamic winding of anyons is widely considered an important candidate for quantum computers. Operations defined by non-Abelian groups are non-commutative in character, meaning that the outcomes of sequential operations depend on the order of execution. Non-Abelian processes can be mathematically captured by unitary matrices, which can manifest as a Berry-phase matrix that connects holonomic adiabatic evolutions of multiple states in parameter space. Because the
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  Trajectory Optimization using the Projection Operator (PRONTO)

  John Hauser, Electrical Computer and Energy Engineering, University of Colorado Boulder

  Apr 2, 12:00 - 13:00

  B9 L2 H2

  Trajectory optimization

  We study the use of a nonlinear projection operator in the development of a novel function space approach for the optimization of trajectory functionals.

Mar 26 - Apr 1, 2023

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  End-to-Same-End Encryption: Modularly Augmenting an App with an Efficient, Portable, and Blind Cloud Storage

  Qiang Tang, Senior Lecturer (equal to U.S. Associate Professor), the University of Sydney

  Mar 30, 12:00 - 13:00

  B4 L5 R5220.

  cloud storage End-to-Same-End-Encryption

  Cloud storage is pervasive nowadays; surprisingly, how to secure cloud storage that is usable in the real world is in fact still open. In this work, we propose a novel system called End-to-Same-End Encryption (E2SEE) that can be deployed directly on existing infrastructure and provide both security and usability. Our system can be flexibly used to augment any App with secure storage, for users to create a personal digital lockbox, and for the cloud to provide secure storage service. A preliminary version of E2SEE was deployed in Snapchat, serving hundreds of millions of users, and the research result was published at USENIX Security 22.
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  On the rate of convergence of the Inviscid Limit for 2D Incompressible Fluid

  Associate Professor Stefano Spirito, Department of Mathematics, University of LAquila, Italy

  Mar 30, 12:00 - 13:00

  B1 L4 R4102

  This talk reviews recent results concerning the inviscid limit for the 2D Euler equations with unbounded vorticity. In particular, by using techniques from the theory of transport equation with no smooth vector fields, we show that the solutions obtained in the vanishing viscosity limit satisfy a representation formula in terms of the flow of the velocity and that the strong convergence of the vorticity holds and we give a rate of convergence.
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  Flexible Multivariate, Spatial, and Causal Models for Extremes

  Yan Gong, Ph.D. Student, Statistics

  Mar 28, 16:00 - 19:00

  B4 L5 R5220

  risk assessment statistical analysis

  Risk assessment for natural hazards and financial extreme events requires the statistical analysis of extreme events, often beyond observed levels. The characterization and extrapolation of the probability of rare events rely on assumptions about the extremal dependence type and about the specific structure of statistical models. In this thesis, we develop models with flexible tail dependence structures, in order to provide a reliable estimation of tail characteristics and risk measures. Our novel methodologies are illustrated by a range of applications to financial, climatic, and health data.
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  Scalable parallel solvers for cardiac reaction-diffusion models and applications

  Luca F. Pavarino, Professor, Department of Mathematics, Università degli Studi di Pavia

  Mar 28, 16:00 - 17:00

  B2 L5 R5220

  Applied Partial Differential Equations parallel simulations

  After a brief introduction to the field of Computational Cardiology and cardiac reentry, we introduce and study some scalable domain decomposition preconditioners for cardiac reaction-diffusion models, discretized with splitting semi-implicit techniques in time and isoparametric finite elements in space.
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  Challenges in ML for Sustainable Development

  Prof. Laure Berti, Computer Science, Research Director, French Institute of Research for Sustainable Development (IRD)

  Mar 28, 14:00 - 15:00

  B1 L4 R4214

  data cleaning and preparation data fusion

  This talk will present recent approaches for mitigation and adaptation, for which data analytics and ML are parts of the solution in a larger context of interdisciplinary and methodological research and innovations.
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  From Contact to Fluid Structure Interavtion Information Transfer between Non-matching meshes for Coupled Simulations

  Prof. Rolf Krause, Università della Svizzera italiana

  Mar 27, 12:00 - 13:00

  B9 L3 R3128

  The transfer of information between non-matching meshes is an important ingredient for coupled multi-physics simulations. For most coupled problems information, such as displacements or stresses, has to be exchanged between, two different meshes. As an example consider fluid-structure interaction (FSI) problem, where information has to be transferred between a fluid and an elastic body (the "S"tructure in FSI).
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  Thoughts on software and systems engineering - 2023-03-27

  Eric Feron, Professor, Electrical and Computer Engineering

  Mar 27, 12:00 - 13:00

  B9 L2 H2 H2

  Complex systems and software engineering, and associated challenges become increasingly important for the well-being and safety of our society of humans. Motivated by this push towards ever more complex systems and software of all sizes, spectacular failures, and decades of questioning in a variety of contexts and endeavors, this talk presents a theory of complex systems engineering, that is, a scientific theory in which an engineered system or software can be seen as a validated scientific hypothesis arising from a convergent mix of mathematical and validated experimental constructs. In its simplest form, a complex engineered system is a manufactured, validated scientific hypothesis arising from a mathematical theorem similar to those found in theoretical physics. This observation provides suggestions for improving system design, especially system architecture, by leveraging advanced mathematical and / or scientific concepts. In return, mathematicians and computer scientists can benefit from this bridge to engineering by bringing to bear many of their automated and manual theorem proving techniques to help with the design of complex systems.
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  Mathematics of Emergent Phenomena in Networks and Collective Motion

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

  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 Science

  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.