Events

Jun 27 - Jun 21, 2020

Mean field games with local coupling (Session I)

Prof. Alessio Porretta, Mathematical Analysis, University of Rome Tor Vergata

Jun 22, 14:00 - 17:00

KAUST

We introduce several PDE tools which are useful in the study of mean field game systems with local couplings. Due to the lack of regularity of solutions, refined compactness and renormalization arguments are needed for a general approach leading to existence and uniqueness results. If time is enough, congestion models will be treated by similar techniques.

Jun 13 - Jun 7, 2020

Trajectory Planning for Autonomous Underwater Vehicles: A Stochastic Optimization Approach

Sultan Albarakati, Director, KAUST Academy

Jun 7, 16:00 - 18:00

KAUST

optimization

In this work, we develop a new framework of trajectory planning for AUVs in realistic ocean scenarios. We divide this work into three parts. In the first part, we provide a new approach for deterministic trajectory planning in steady current, described using Ocean General Circulation Model (OGCM) data. The latter are used to specify both the ocean current and the bathymetry. We apply a NLP to the optimal-time trajectory planning problem. To demonstrate the effectivity of the resulting model, we consider the optimal time trajectory planning of an AUV operating in the Red Sea and the Gulf of Aden. In the second part, we generalize our 3D trajectory planning framework to time-dependent ocean currents. We also extend the framework to accommodate multi-objective criteria, focusing specifically on the Pareto front curve between time and energy. The scheme is demonstrated for time-energy trajectory planning problems in the Gulf of Aden. In the last part, we address uncertainty in the ocean current field. The uncertainty in the current is described in terms of a finite ensemble of flow realizations. The proposed approach is based on a non-linear stochastic programming methodology that uses a risk-aware objective function, accounting for the full variability of the flow ensemble. Advanced visualization tools are used to amplify simulation results.

May 2 - Apr 26, 2020

On Optimal Regularization in Estimation, Detection, and Classification

Tareq Al-Naffouri, Professor, Electrical and Computer Engineering

Apr 30, 12:00 - 13:00

KAUST

linear systems computational Complexity Convex Gaussian Min-max theorem

In many problems in statistical signal processing, regularization is employed to deal with uncertainty, ill-posedness, and insufficiency of training data. It is possible to tune these regularizers optimally asymptotically, i.e. when the dimension of the problem becomes very large, by using tools from random matrix theory and Gauss Process Theory. In this talk, we demonstrate the optimal turning of regularization for three problems : i) Regularized least squares for solving ill-posed and/or uncertain linear systems, 2) Regularized least squares for signal detection in multiple antenna communication systems and 3) Regularized linear and quadratic discriminant binary classifiers.

Apr 25 - Apr 19, 2020

Efficient and Physics-Preserving Discretization Schemes for Multiphase Flow Problems

Apr 23, 12:00 - 13:00

KAUST

No summary is available.

Apr 18 - Apr 12, 2020

Quantitative understanding of transcription factor regulation at network- and molecular-levels through optimization and deep learning

Xin Gao, Professor, Computer Science

Apr 16, 12:00 - 13:00

KAUST

transcription factor optimization Deep learning

Transcription factors are an important family of proteins that control the transcription rate from DNAs to messenger RNAs through the binding to specific DNA sequences. Transcription factor regulation is thus fundamental to understanding not only the system-level behaviors of gene regulatory networks, but also the molecular mechanisms underpinning endogenous gene regulation. In this talk, I will introduce our efforts on developing novel optimization and deep learning methods to quantitatively understanding transcription factor regulation at network- and molecular-levels. Specifically, I will talk about how we estimate the kinetic parameters from sparse time-series readout of gene circuit models, and how we model the relationship between the transcription factor binding sites and their binding affinities.

Apr 11 - Apr 5, 2020

Geometry and Computational Design

Apr 9, 12:00 - 13:00

KAUST

Geometry computational design

An important stream of research in computational design aims at digital tools which support users in realizing their design intent in a simple and intuitive way, while simultaneously taking care of key aspects of function and fabrication. Such tools are expected to shorten the product development cycle through a reduction of costly feedback loops between design, engineering and fabrication. The strong coupling between shape generation, function and fabrication is a rich source for the development of new geometric concepts, with an impact to the original applications as well as to geometric theory. This will be illustrated at hand of applications in architecture and fabrication with a mathematical focus on discrete differential geometry and geometric optimization problems.

Apr 4 - Mar 29, 2020

Classification of Functional Data by Detecting the Discrepancy of Second Moment Structure of Scaled functions

Shuhao Jiao, Postdoctoral Research Fellow, Statistics

Apr 2, 12:00 - 13:00

KAUST

Functional data

This talk presents a new classification method for functional data. We consider the case where different groups of functions have similar means so that it is difficult to classify them based on only the mean function. To overcome this limitation, we propose the second moment based functional classifier (SMFC). Here, we demonstrate that the new method is sensitive to divergence in the second moment structure and thus produces lower rate of misclassification compared to other competitor methods. Our method uses the Hilbert-Schmidt norm to measure the divergence of second moment structure. One important innovation of our classification procedure lies in the dimension reduction step. The method data-adaptively discovers the basis functions that best capture the discrepancy between the second moment structures of the groups, rather than uses the functional principal component of each individual group, and good performance can be achieved as unnecessary variability is removed so that the classification accuracy is improved. Consistency properties of the classification procedure and the relevant estimators are established. Simulation study and real data analysis on phoneme and rat brain activity trajectories empirically validate the superiority of the proposed method.

Mar 14 - Mar 8, 2020

Functional data depth and its application in the visualization of spatio-temporal covariance structures

Mar 12, 12:00 - 13:00

KAUST

functional data analysis spatio-temporal statistics

Functional data analysis is a very active research area due to the overwhelming existence of functional data. In the first part of this talk, I will introduce how functional data depth is used to carry out exploratory data analysis and explain recently-developed depth techniques. In the second part, I will discuss spatio-temporal statistical modeling. It is challenging to build realistic space-time models and assess the validity of the model, especially when datasets are large. I will present a set of visualization tools we developed using functional data analysis techniques for visualizing covariance structures of univariate and multivariate spatio-temporal processes. I will illustrate the performance of the proposed methods in the exploratory data analysis of spatio-temporal data.

Mar 7 - Mar 1, 2020

Simulation of Neuronal Signal Processing - 2020-03-05

Gabriel Wittum, Professor (former), Applied Mathematics and Computational Science

Mar 5, 12:00 - 13:00

B9 L2 R2322

Neuronal ensembles Stochastiic Variability neuroscience

In the lecture we present a three dimensional mdoel for the simulation of signal processing in neurons. To handle problems of this complexity, new mathematical methods and software tools are required. In recent years, new approaches such as parallel adaptive multigrid methods and corresponding software tools have been developed allowing to treat problems of huge complexity. Part of this approach is a method to reconstruct the geometric structure of neurons from data measured by 2-photon microscopy. Being able to reconstruct neural geometries and network connectivities from measured data is the basis of understanding coding of motoric perceptions and long term plasticity which is one of the main topics of neuroscience. Other issues are compartment models and upscaling.

Feb 29 - Feb 23, 2020

Deterministic particle approximation for nonlinear transport equations

Marco Di Francesco, Associate Professor, Department of Information Engineering, Computer Science and Mathematics, University of L'Aquila (Italy)

Feb 25, 14:00 - 15:00

Building L3 R3119

PDE numerical methods nonlinear models

Approximating the solution to an evolutionary partial differential equation by a set of "moving particles" has several advantages. It validates the use of a continuity equation in an "individuals-based" modeling setting, it provides a link between Lagrangian and Eulerian description, and it defines a "natural" numerical approach to those equations. I will describe recent rigorous results in that context. The main one deals with one-dimensional scalar conservation laws with nonnegative initial data, for which we prove that the a suitably designed "follow-the-leader" particle scheme approximates entropy solutions in the sense of Kruzkov in the many particle limit. Said result represents a new way to solve scalar conservation laws with bounded and integrable initial data. The same method applies to second order traffic flow models, to nonlocal transport equations, and to the Hughes model for pedestrian movements.

Feb 22 - Feb 16, 2020

Feb 15 - Feb 9, 2020

Feb 8 - Feb 2, 2020

Feb 1 - Jan 26, 2020

Dynamics and Convergence of Weight Normalization for Training Neural Networks

Guido Montufar, Assistant Professor, Departments of Mathematics and Statistics, University of California, Los Angeles (UCLA)

Jan 29, 13:00 - 14:30

B1 L3 R3119

machine learning neural network optimization ReLUs

We present a result on the convergence of weight normalized training of artificial neural networks. In the analysis, we consider over-parameterized 2-layer networks with rectified linear units (ReLUs) initialized at random and trained with batch gradient descent and a fixed step size. The proof builds on recent theoretical works that bound the trajectory of parameters from their initialization and monitor the network predictions via the evolution of a ''neural tangent kernel'' (Jacot et al. 2018). We discover that training with weight normalization decomposes such a kernel via the so called ''length-direction decoupling''. This in turn leads to two convergence regimes. From the modified convergence we make a few curious observations including a natural form of ''lazy training'' where the direction of each weight vector remains stationary.

Jan 25 - Jan 19, 2020

A very short introduction to regularity theory for linear elliptic equations - Part 4

Professor Jose Urbano, Department of Mathematics at University of Coimbra, Portugal

Jan 22, 14:00 - 15:30

B1 L3 R3119

PDE regularity

Mini Course Part 4 of 4. The course is a very short introduction to regularity for linear elliptic pdes of second order. We start with equations with regular coefficients and the difference quotient method of Nirenberg. We then treat the case of coefficients that are merely measurable and bounded, putting forward the basics of De Giorgi-Nash-Moser theory. If time permits, we present some characterizations of Hölder spaces which are very useful in regularity theory.
A very short introduction to regularity theory for linear elliptic equations - Part 3

Professor Jose Urbano, Department of Mathematics at University of Coimbra, Portugal

Jan 20, 14:00 - 15:30

B1 L3 R3119

PDE regularity

Mini Course Part 3 of 4. The course is a very short introduction to regularity for linear elliptic pdes of second order. We start with equations with regular coefficients and the difference quotient method of Nirenberg. We then treat the case of coefficients that are merely measurable and bounded, putting forward the basics of De Giorgi-Nash-Moser theory. If time permits, we present some characterizations of Hölder spaces which are very useful in regularity theory.
KAUST Research Conference Digital Health 2020

Takashi Gojobori, Ibn Sina Distinguished Professor (former), Bioscience

Jan 20, 08:00 - Jan 22, 17:00

B19 L2 H1

digital health artificial intelligence computational science big data analytics Omics

Computational Bioscience Research Center at King Abdullah University of Science and Technology is pleased to announce the KAUST Research Conference on Digital Health 2020.

Jan 18 - Jan 12, 2020

A very short introduction to regularity theory for linear elliptic equations - Part 2

Professor Jose Urbano, Department of Mathematics at University of Coimbra, Portugal

Jan 15, 14:00 - 15:30

B1 L2 R2202

PDE regularity

Mini Course Part 2 of 4. The course is a very short introduction to regularity for linear elliptic pdes of second order. We start with equations with regular coefficients and the difference quotient method of Nirenberg. We then treat the case of coefficients that are merely measurable and bounded, putting forward the basics of De Giorgi-Nash-Moser theory. If time permits, we present some characterizations of Hölder spaces which are very useful in regularity theory.
A very short introduction to regularity theory for linear elliptic equations - Part 1

Professor Jose Urbano, Department of Mathematics at University of Coimbra, Portugal

Jan 13, 14:00 - 15:30

B1 L3 R3119

PDE regularity

Mini Course Part 1 of 4. The course is a very short introduction to regularity for linear elliptic pdes of second order. We start with equations with regular coefficients and the difference quotient method of Nirenberg. We then treat the case of coefficients that are merely measurable and bounded, putting forward the basics of De Giorgi-Nash-Moser theory. If time permits, we present some characterizations of Hölder spaces which are very useful in regularity theory.
Well-posedness and stability results for laminated Timoshenko beams with interfacial slip and infinite memory

Prof. Aissa Guesmia, University of Lorraine, Metz, France

Jan 12, 10:00 - 11:00

B1 L4 R4214

Abstract The model under consideration in this work describes a vibrating structure of an interfacial slip and consists of three coupled hyperbolic equations in one-dimensional bounded interval under mixed homogeneous Dirichlet-Neumann boundary conditions. The first two equations are related to Timo-shenko type systems, and the third one is subject to the dynamics of the slip. The main problem we discuss here is stabilizing the system by a viscoelastic damping generated by an infinite memory and acting only on one equation. First, we prove the existence, uniqueness and regularity of solutions

Dec 7 - Dec 1, 2019

Nov 30 - Nov 24, 2019

Nov 23 - Nov 17, 2019

Nov 16 - Nov 10, 2019

Nov 9 - Nov 3, 2019

Nov 2 - Oct 27, 2019

Energy-conserving and energy-stable discretizations of differential equations

David Ketcheson, Professor, Applied Mathematics and Computational Science

Oct 31, 12:00 - 13:00

B9 L2 H1 R2322

numerical discretization energy stability energy conservation Runge-Kutta methods

Abstract Many physical models are characterized by the property that some measure of energy is conserved or is non-increasing in time. This property may be challenging to guarantee in numerical discretizations; often it requires the use of costly implicit methods. I will review some approaches to energy stability and describe a new approach that guarantees energy stability with a general class of explicit schemes that are a simple modification of standard Runge-Kutta methods. Brief Biography David Ketcheson is an Associate Professor of Applied Mathematics and Computational Science (AMCS) and

Oct 26 - Oct 20, 2019

Selected applications of splines of arbitrary manifold topology

Pieter Barendrecht, PhD Student, Computer Science, University of Groningen, The Netherlands

Oct 24, 14:00 - 15:00

B1 L4 R4214

Abstract There are many intriguing aspects and applications of splines, in particular in the setting of arbitrary manifold topology. In this talk we will take a look at a recently published spline-based subdivision scheme for three-valent meshes (i.e. meshes composed of mostly hexagons), providing us with a good understanding of subdivision surfaces in general. We then move on to study improved quadrature rules for the well-known Catmull-Clark subdivision scheme for quad-dominant meshes, something long-awaited by spline-based numerical methods such as isogeometric analysis. Finally, we shift

Oct 19 - Oct 13, 2019

Monte Carlo Computation

Oct 17, 12:00 - 13:00

B9 L2 H1 R2322

Abstract Abstract text. Brief Biography Ajay Jasra received his Ph.D. degree in statistics from Imperial College London in 2005. Since 2011 he has been a tenured associate professor at the Department of Statistics and Applied Probability at the National University of Singapore (NUS). Between 2005-2008 he has held various post-doctoral positions at the University of Oxford, the University of Cambridge and the Institute of Statistical Mathematics in Tokyo. He was also Chapman Fellow of Mathematics at Imperial College London in that period. Between 2008-2011 he was assistant professor, with

Oct 12 - Oct 6, 2019

Modeling, studying, and forecasting the circulation and Climate of the Red Sea

Ibrahim Hoteit, Professor, Earth Science and Engineering

Oct 10, 12:00 - 13:00

B9 L2 H1 R2322

Abstract The talk will present the integrated data-driven modeling system that we are developed to study and predict the circulation and the climate of the Red Sea. I will first describe the modeling system and summarize our key findings on the Red Sea general circulation, including its dominant eddy activity, the striking seasonally overturning circulation, and the occasional northern deep water formation events, and discuss their impact on the Red Sea ecosystem. I will then focus on our efforts to develop a regional ensemble data assimilation and forecasting system, presenting recent

Sep 21 - Sep 15, 2019

Sep 14 - Sep 8, 2019

Waves in Complex Media: Theories and Applications

Ying Wu, Program Chair, Applied Mathematics and Computational Science

Sep 12, 12:00 - 13:00

B9 L2 H1

We focus on the theoretical modeling and numerical simulation of classical wave propagation in complex systems, such as periodic structures and random media. In this talk, I will give an overview of the research conducted in our group by emphasizing on three major aspects: numerical method, homogenization, and applications in artificial materials.

Sep 7 - Sep 1, 2019

Interesting Problems in Estimation and Control on Smart Road Vehicles

Professor Rajesh Rajamani, Mechanical Engineering, University of Minnesota

Sep 1, 11:00 - 12:00

B1 L2 R4214

Estimation Theory autonomous driving University of Minnesota

A number of exciting vehicle automation and active safety systems are being developed by research groups around the world. This talk focuses on novel sensors, estimation algorithms and control systems that can fill critical gaps in the automation technologies under development. The first part of this seminar describes interesting sensing and estimation solutions that can significantly improve the effectiveness of active safety systems. The second part of the seminar describes the development of a new class of narrow commuter vehicles designed to address traffic congestion, improve highway mobility and provide very high fuel economy. The final part of the seminar describes the development of a smart bicycle with instrumentation that can track trajectories of nearby vehicles on the road and provide warnings to the motorist, if a potential car-bicycle collision is detected.

Aug 3 - Jul 28, 2019