Events

Nov 15 - Nov 21, 2020

Start your Research Journey at KAUST - Master and Ph.D. degrees in Applied Mathematics, Computer Science, Electrical Engineering and Statistics

Aigars Ekers, Dean, University of Latvia, Faculty of Science and Technology

Nov 18, 16:00 - 17:00

KAUST

Did you know that courses are taught in English at KAUST? Did you know that the faculty and students are from more than 100 countries? Did you know that you will be fully funded when studying at KAUST? What you will learn from this webinar: Life at KAUST: What are KAUST and CEMSE? Degrees and Academic Programs Research Areas and Lab Facilities Eligibility and Admission Requirements KAUST Fellowship: Benefits of studying at KAUST How to Apply?
Federated Learning of a Mixture of Global and Local Models

Filip Hanzely, Ph.D., Applied Mathematics and Computational Sciences

Nov 16, 12:00 - 13:00

KAUST

We propose a new optimization formulation for training federated learning models. The standard formulation has the form of an empirical risk minimization problem constructed to find a single global model trained from the private data stored across all participating devices. In contrast, our formulation seeks an explicit trade-off between this traditional global model and the local models, which can be learned by each device from its own private data without any communication. Further, we develop several efficient variants of SGD (with and without partial participation and with and without variance reduction) for solving the new formulation and prove communication complexity guarantees.

Nov 8 - Nov 14, 2020

Nov 1 - Nov 7, 2020

Oct 25 - Oct 31, 2020

On the approximation of the spectrum of differential operators

Daniele Boffi, Associate Dean for Faculty, Computer, Electrical and Mathematical Sciences and Engineering

Oct 29, 12:00 - 13:00

KAUST

Eigenvalue problems associated with partial differential equations are key ingredients for the modeling and simulation of a variety of real world applications, ranging from fluid-dynamics, structural mechanics, acoustics, to electromagnetism and medical problems. We review some properties related to the approximation of eigenvalue problems. Starting from matrix algebraic problems, we present a series of examples and counterexamples showing that even extremely simple situations can produce unexpected results.

Oct 18 - Oct 24, 2020

Efficiency Assessment of Approximated Spatial Predictions for Large Datasets

Yiping Hong, Postdoctoral Research Fellow, Statistics

Oct 22, 12:00 - 13:00

KAUST

mathematics applied statistics Social Network Analysis

Our suggested criteria are more useful for the determination of tuning parameters for sophisticated approximation methods of spatial model fitting. To illustrate this, we investigate the trade-off between the execution time, estimation accuracy, and prediction efficiency for the TLR method with intensive simulation studies and suggest proper settings of the TLR tuning parameters.

Oct 11 - Oct 17, 2020

Oct 4 - Oct 10, 2020

The Infinity Laplacian (Session II)

Prof. José Miguel Urbano, Department of Mathematics, University of Coimbra, Portugal

Oct 8, 16:00 - 17:30

KAUST

The mini-course is an introduction to the analysis of infinity-harmonic functions. We detail the proof of the equivalence between enjoying comparison with cones and solving the infinity-Laplace equation in the viscosity sense, thus making a seamless connection with the previous mini-course. Further material includes the existence of infinity-harmonic functions in the case of an unbounded domain and an easy and self-contained proof, due to Armstrong and Smart, of the celebrated uniqueness theorem of Jensen.
ExaGeoStat: Moving Towards Large-Scale Geostatistical Modeling on Manycore Systems

Sameh Abdulah, Sameh Abdulah > Appointments, Roles and Affiliations, Hierarchical Computations on Manycore Architectures

Oct 8, 12:00 - 13:00

KAUST

Adaptive modeling

We present Exascale GeoStatistics (ExaGeoStat) software, a high-performance library implemented on a wide variety of contemporary hybrid distributed-shared supercomputers whose primary target is climate and environmental prediction applications.
The Infinity Laplacian (Session I)

Prof. José Miguel Urbano, Department of Mathematics, University of Coimbra, Portugal

Oct 6, 16:00 - 17:30

KAUST

The mini-course is an introduction to the analysis of infinity-harmonic functions. We detail the proof of the equivalence between enjoying comparison with cones and solving the infinity-Laplace equation in the viscosity sense, thus making a seamless connection with the previous mini-course. Further material includes the existence of infinity-harmonic functions in the case of an unbounded domain and an easy and self-contained proof, due to Armstrong and Smart, of the celebrated uniqueness theorem of Jensen.

Sep 27 - Oct 3, 2020

Sep 13 - Sep 19, 2020

Sep 6 - Sep 12, 2020

Aug 30 - Sep 5, 2020

The two faces of correlation

Haavard Rue, Program Chair, Statistics

Sep 3, 12:00 - 13:00

KAUST

Statistical Modeling computing

Discussing the concept of correlation and how to interpret it alone (marginally) or within a more complex environment (conditionally). This rather simple observation is the key observation behind a lot of exciting developments and connections in statistics that can be leveraged for improved computations and better motivated statistical models.

Jul 26 - Aug 1, 2020

Optimization for Supervised Machine Learning: Randomized Algorithms for Data and Parameters

Filip Hanzely, Ph.D., Applied Mathematics and Computational Sciences

Jul 30, 16:00 - 18:00

KAUST

Many key problems in machine learning and data science are routinely modeled as optimization problems and solved via optimization algorithms. With the increase of the volume of data and the size and complexity of the statistical models used to formulate these often ill-conditioned optimization tasks, there is a need for new efficient algorithms able to cope with these challenges. In this thesis, we deal with each of these sources of difficulty in a different way. To efficiently address the big data issue, we develop new methods which in each iteration examine a small random subset of the training data only. To handle the big model issue, we develop methods which in each iteration update a random subset of the model parameters only. Finally, to deal with ill-conditioned problems, we devise methods that incorporate either higher-order information or Nesterov's acceleration/momentum. In all cases, randomness is viewed as a powerful algorithmic tool that we tune, both in theory and in experiments, to achieve the best results.

Jul 12 - Jul 18, 2020

Jul 5 - Jul 11, 2020

Jun 28 - Jul 4, 2020

Jun 21 - Jun 27, 2020

Mean Field Games with Local Coupling (Session II)

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

Jun 25, 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.
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 7 - Jun 13, 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.

Apr 26 - May 2, 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 19 - Apr 25, 2020

Efficient and Physics-Preserving Discretization Schemes for Multiphase Flow Problems

Shuyu Sun, Professor, Earth Science and Engineering

Apr 23, 12:00 - 13:00

KAUST

No summary is available.

Apr 12 - Apr 18, 2020

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

Xin Gao, Program Chair, 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 5 - Apr 11, 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.

Mar 29 - Apr 4, 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 8 - Mar 14, 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 1 - Mar 7, 2020

Simulation of Neuronal Signal Processing - 2020-03-05

Gabriel Wittum, Professor, Applied Mathematics and Computational Sciences

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 23 - Feb 29, 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 16 - Feb 22, 2020