Tuesday, November 22, 2022, 12:00
- 13:00
Building 9, Level 2, Room 2322
Contact Person
Infinity-harmonic functions have recently found application in Semi-Supervised Learning, in the context of the so-called Lipschitz Learning. With this application in mind, we will discuss the Lipschitz extension problem, its solution via MacShane-Whitney extensions and its several drawbacks, leading to the notion of AMLE (Absolutely Minimising Lipschitz Extension).
Monday, November 21, 2022, 12:00
- 13:00
Building 9, Level 2, Room 2322, Hall 1
Contact Person
In this talk, I will first give a convergence analysis of gradient descent (GD) method for training neural networks by relating them with finite element method. I will then present some acceleration techniques for GD method and also give some alternative training algorithms
Computational Bioscience Research Center
Monday, November 21, 2022, 09:00
- 17:00
Virtual
Contact Person
The Computational Bioscience Research Center (CBRC) at King Abdullah University of Science and Technology (KAUST) is pleased to host the International Conference on Bioinformatics 2022 (InCoB2022).  This year’s conference theme will be “Accelerating innovation to meet biological challenges: The role of bioinformatics”.
Sunday, November 20, 2022, 12:00
- 13:00
Building 9, Level 2, Room 2322 (Lecture Hall 1)
Contact Person
A multi-agent system consists of individual agents sharing information and coordinating for collective decision making. The study of multi-agent decision making has important implications in conceiving networked engineering systems - a team of mobile robots or a fleet of drones - that can effectively coordinate to carry out assigned missions. Modeling such system as feedback interconnections of many smaller units allows us to examine its long-term behavior using analytical tools from feedback control theory, such as Lyapunov stability and bifurcation analysis. In this presentation, we discuss how such tools can be used to predict asymptotic behavior of the agents' decision making process and also to design computational models of the decision making process.
Ghulam Qadir, Posdoctoral fellow, Computational Statistics group at Heidelberg Institute for Theoretical Studies, Germany
Thursday, November 17, 2022, 10:00
- 11:00
Building 1, Level 4, Room 4102
Contact Person
Statistical analysis for the purpose of prediction is preferably accompanied by uncertainty quantification, often in the form of prediction intervals. Deep learning approaches have been extensively shown to provide accurate point predictions in many applications.
Tuesday, November 15, 2022, 12:00
- 13:00
Building 9, Level 2, Room 2322
Contact Person
The talk will give an overview of recent results for models of collective behavior governed by functional differential equations. It 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.
Prof. Haithem Taha, Associate Professor, Department of Mechanical and Aerospace Engineering at the University of California, Irvine
Monday, November 14, 2022, 12:00
- 13:00
Building 2, Level 5, Room 5220
Contact Person
In this talk, I will present a special variational principle that we revived from the history of analytical mechanics: Hertz’ principle of least curvature. Using this principle, we developed a general (dynamical) closure condition that is, unlike the Kutta condition, derived from first principles. In contrast to the classical theory, the proposed variational theory is not confined to sharp-edged airfoils, i.e., it allows, for the first time, theoretical computation of lift over arbitrarily smooth shapes, thereby generalizing the century-old lift theory of Kutta and Zhukovsky. Moreover, the new variational condition reduces to the Kutta condition in the special case of a sharp-edged airfoil, which challenges the widely accepted concept regarding the viscous nature of the Kutta condition.  We also generalized this variational principle to Navier-Stokes’, thereby discovering the fundamental quantity that Nature minimizes in every incompressible flow.
Francesco Orabona, Associate Professor of Electrical and Computer Engineering, Boston University
Monday, November 14, 2022, 12:00
- 13:00
Building 9, Level 2, Room 2322
Contact Person
Parameter-free online optimization is a class of algorithms that does not require tuning hyperparameters, yet they achieve the theoretical optimal performance. Moreover, they often achieve state-of-the-art performance too. An example would be gradient descent algorithms completely without learning rates. In this talk, I review my past and present contributions to this field. Building upon a fundamental idea connecting optimization, gambling, and information theory, I discuss selected applications of parameter-free algorithms to machine learning and statistics. Finally, we conclude with an overview of the future directions of this field.
Sunday, November 13, 2022, 12:00
- 13:00
Building 9, Level 2, Room 2322, Lecture Hall 1
Contact Person
In this talk, I will discuss multi-functional smart electronic devices which can perform the roles of multiple conventionally discrete components at once.
Prof. Michal Mankowski, Assistant Professor of Operations Research, Erasmus University Rotterdam, Netherlands
Thursday, November 10, 2022, 10:00
- 11:30
Building 1, Level 3, Room 3119
Contact Person
The aim of this course is to familiarize the students with the usage of Computer Simulation tools for complex problems. The course will introduce the basic concepts of computation through modeling and simulation that are increasingly being used in industry and academia. The basic concepts of Discrete Event Simulation will be introduced along with the reliable methods of random variate generation and variance reduction. Later in the course, the concept of simulation-based optimization and output analysis will be discussed. The example of simulation (and optimization) applied to design an optimal organ allocation policy in the US will be discussed.
Prof. Michal Mankowski, Assistant Professor of Operations Research, Erasmus University Rotterdam, Netherlands
Wednesday, November 09, 2022, 10:00
- 11:30
Building 1, Level 3, Room 3119
Contact Person
The aim of this course is to familiarize the students with the usage of Computer Simulation tools for complex problems. The course will introduce the basic concepts of computation through modeling and simulation that are increasingly being used in industry and academia. The basic concepts of Discrete Event Simulation will be introduced along with the reliable methods of random variate generation and variance reduction. Later in the course, the concept of simulation-based optimization and output analysis will be discussed. The example of simulation (and optimization) applied to design an optimal organ allocation policy in the US will be discussed.
Prof. Simone Scacchi, Associate Professor of Numerical Analysis at the Department of Mathematics of the University of Milan
Tuesday, November 08, 2022, 15:30
- 17:00
Building 1, Level 3, Room 3119
Contact Person
In this seminar, we will present our work on Virtual Element Method (VEM) approximations. The Virtual Element Method is a recent numerical technique for solving partial differential equations on computational grids constituted by polygonal or polyhedral elements of very general shape. This work aims to develop effective linear solvers for general order VEM approximations of three-dimensional scalar elliptic equations in mixed form and Stokes equations. To this end, we consider block algebraic multigrid preconditioners and balancing domain decomposition by constraints (BDDC) preconditioners. The latter allows us to use conjugate gradient iterations, albeit the algebraic linear systems arising from the discretization of the differential problems are indefinite, ill-conditioned, and of saddle point nature.
Daniele Durante, Assistant Professor of Statistics at the Department of Decision Sciences, Bocconi University, Italy
Tuesday, November 08, 2022, 15:00
- 16:00
Building 1, Level 4, Room 4102
Contact Person
In this talk, I will review, unify and extend recent advances in Bayesian inference and computation for such a class of models, proving that unified skew-normal (SUN) distributions (which include Gaussians as a special case) are conjugate to the general form of the likelihood induced by these formulations. This result opens new avenues for improved posterior inference, under a broad class of widely-implemented models, via novel closed-form expressions, tractable Monte Carlo methods based on independent and identically distributed samples from the exact SUN posterior, and more accurate and scalable approximations from variational Bayes and expectation-propagation. These results will be further extended, in asymptotic regimes, to the whole class of Bayesian generalized linear models via novel limiting approximations relying on skew-symmetric distributions.
Tuesday, November 08, 2022, 12:00
- 13:00
Building 9, Level 2, Room 2322
Contact Person
Surface water waves are a physically important phenomenon with which we all have some experience. They are also surprisingly complex and interesting from a mathematical perspective. I will discuss two recent projects in water wave modeling. The first deals with ocean waves, such as tsunamis, passing over the continental slope. It has long been known that the amplification of such waves is greater than what the traditional transmission coefficient would predict.
Prof. Michal Mankowski, Assistant Professor of Operations Research, Erasmus University Rotterdam, Netherlands
Tuesday, November 08, 2022, 10:00
- 11:30
Building 1, Level 3, Room 3119
Contact Person
The aim of this course is to familiarize the students with the usage of Computer Simulation tools for complex problems. The course will introduce the basic concepts of computation through modeling and simulation that are increasingly being used in industry and academia. The basic concepts of Discrete Event Simulation will be introduced along with the reliable methods of random variate generation and variance reduction. Later in the course, the concept of simulation-based optimization and output analysis will be discussed. The example of simulation (and optimization) applied to design an optimal organ allocation policy in the US will be discussed.
Monday, November 07, 2022, 14:00
- 16:00
Building 1, Level 2, Room 2202
Contact Person
In this thesis, we present three projects. First, we investigate the numerical approximation of Hamilton-Jacobi equations with the Caputo time-fractional derivative. We introduce an explicit in time discretization of the Caputo derivative and a finite-difference scheme for the approximation of the Hamiltonian. We show that the approximation scheme is stable under an appropriate condition on the discretization parameters and converges to the unique viscosity solution of the Hamilton-Jacobi equation.
Tobias Isenberg, Senior Research Scientist, Inria
Monday, November 07, 2022, 12:00
- 13:00
Building 9, Level 2, Room 2322, Hall 1
Contact Person
In this talk I will report on various research projects that I carried out with my students to better understand the interaction landscape and will report on lessons we learned. I will focus mostly on AR-based setups with application examples from physical flow visualization, molecular visualization, visualization of particle collisions, biomolecular dynamics in cells, and oceanography. I will show interaction techniques that rely on purely gestural interaction, phones or tablets as input and control devices, and hybrid setups that combine traditional workstations with AR views. I will discuss navigation, data selection, and visualization system control as different interaction tasks. With this overview I aim to provide an understanding of typical challenges in immersive visualization environments and how to address some of these challenges.
Monday, November 07, 2022, 11:00
- 13:00
Building 3, Level 5, Room 5220; https://kaust.zoom.us/j/95838296527
Contact Person
As a branch of statistics, functional data analysis studies observations regarded as curves, surfaces, or other objects evolving over a continuum. Current methods in functional data analysis usually require data to be smoothed and analyzed marginally, which may hide some outlier information or take extra time on pretreating the data. After exploring model-based fitting for regularly observed multivariate functional data, we explore new visualization tools, clustering, and multivariate functional depths for irregularly observed (sparse) multivariate functional data.
Giovanni Geraci, Assistant Professor, Pompeu Fabra University, Barcelona
Sunday, November 06, 2022, 16:00
- 17:00
Building 1, Level 3, Room 3119
Contact Person
In a quest for anything, anytime, anywhere connectivity, next-generation wireless networks are envisioned to break the boundaries of the current ground-focused paradigm and fully embrace aerial and spaceborne communications.
Mohannad Alhazmi, Electrical Engineering, King Saud University
Sunday, November 06, 2022, 12:00
- 13:00
Building 9, Level 2, Room 2322, Lecture Hall 1
Contact Person
With the widespread deployment of advanced heterogeneous technologies and the sharply-growing complexity in our modern society, there is an increasing demand for risk-aware management and joint operation of interconnected infrastructures and lifeline networks.
Prof. Jae-Hyun Ryou, Mechanical Engineering, University of Houston
Thursday, November 03, 2022, 12:00
- 13:00
Building 9, Level 3, 3223
Contact Person
Flexible electronics is an emerging and widely explored area.  Most research groups in the area focus on fabrication processes to provide mechanical flexibility and their use in bendable and stretchable applications.  Also, most semiconductors employed in flexible electronics are non-single-crystalline thin films which compromise the performance of the flexible devices.
Wednesday, November 02, 2022, 15:30
- 17:30
Building 2, Level 5, Room 5220
Contact Person
This dissertation presents novel approaches to the design of electrical and optical wide bandgap semiconductor devices, which opens new avenues for future research. It is possible that it might be used in a broad variety of sectors, including illumination, sensing, disinfection, and power devices by using TCAD and machine learning to deliver quick forecasts of the III-nitride semiconductor device.
Prof. Gonçalo dos Reis, School of Mathematics, University of Edinburgh
Tuesday, November 01, 2022, 15:30
- 17:00
Building 1, Level 3, Room 3119
Contact Person
We propose a novel approach of numerically approximate McKean-Vlasov SDEs that avoids the usual interacting particle approximation and Propagation of Chaos results altogether.