Events

Feb 26 - Mar 4, 2023

Extremes of stationary time-series - 2023-02-09

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

Feb 28, 09:00 - 16:00

B1 L4 R4102

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

Ying Wu, Program Chair, Applied Mathematics and Computational Science

Feb 27, 12:00 - 13:00

B9 L2 H2 H2

acoustic waves

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

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

Feb 27, 09:00 - 16:00

B1 L4 R4102

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

Feb 12 - Feb 18, 2023

Conditional Kernel Density Estimation For Dependent Data, Mean Shift Algorithm and Predictive Regions in Human Health

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

Feb 12, 16:00 - 17:30

B1 L4 R4102

nonparametric estimation

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

Feb 5 - Feb 11, 2023

Point processes and their application to modeling drug seizure and overdose interactions

Prof. George Mohler, Computer Science, Boston College

Feb 8, 17:00 - 18:00

KAUST

point processes stochastic models

In this talk we first provide an introduction to point processes, which are stochastic models for the occurrence of events in space and time. We then discuss the application of point processes to investigate the relationship between law enforcement drug seizures and accidental overdoses in Indianapolis. We will also discuss results from a field-experiment in Indianapolis where point process based harm indices were used to inform the distribution of addiction treatment information.

Jan 29 - Feb 4, 2023

Driving Innovation by Simulation

Dr. Emad Dlala, Vice President Powertrain, Lucid Motors Co.

Jan 30, 12:00 - 13:00

B9 L3 R3128

Driving innovation aerodynamic simulations

Dr. Emad Dlala is Vice President of Powertrain at Lucid. He is responsible for powertrain development and engineering, including batteries, drive units, and power electronics. He also leads Lucid’s systems efficiency strategy. Emad previously served as Senior Director of Efficiency and Energy Technology. Emad has been with Lucid in various technical roles since 2015 where he’s led efforts to develop breakthrough technologies that achieved unmatched range and performance in the Lucid Air. Emad brings more than two decades of engineering experience to his current leadership role. Prior to Lucid, he worked as a Senior Application Engineer at ANSYS Inc. developing cutting-edge, computer-based engineering simulation technologies. Emad holds a Ph.D. and M.Sc. in Electrical Engineering from Aalto University, Finland, and a B.Sc. in Electrical Engineering from Al-Zawiya University, Libya.

Jan 1 - Jan 7, 2023

Bayesian Non-parametric Models for Time Series Decomposition

Guillermo C. Granados Garcia, Ph.D. Student, Statistics

Jan 5, 17:00 - 19:00

B1 R4214

spectral density function

The standard approach to analyzing brain electrical activity is to examine the spectral density function (SDF) and identify frequency bands, defined apriori, that have the most substantial relative contributions to the overall variance of the signal. However, a limitation of this approach is that the precise frequency and bandwidth of oscillations are not uniform across cognitive demands. Thus, these bands should not be arbitrarily set in any analysis. To overcome this limitation, we propose three Bayesian Non-parametric models for time series decomposition, which are data-driven approaches that identify (i) the number of prominent spectral peaks, (ii) the frequency peak locations, and (iii) their corresponding bandwidths (or spread of power around the peaks).

Dec 4 - Dec 10, 2022

Modelling and Simulation of Biological Systems - 2022-12-06

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

Dec 6, 12:00 - 13:00

B9 L2 R2322

Biological systems

Biological systems are distinguished by their enormous complexity and variability. That is why mathematical modelling and computational simulation of those systems is very difficult, in particular thinking of detailed models which are based on first principles. The difficulties start with geometric modelling which needs to extract basic structures from highly complex and variable phenotypes, on the other hand also has to take the statistic variability into account.

Nov 27 - Dec 3, 2022

Nov 20 - Nov 26, 2022

Lipschitz learning and the infinity-Laplacian

Miguel Urbano, Professor, Applied Mathematics and Computational Science

Nov 22, 12:00 - 13:00

B9 L2 R2322

Lipschitz additive nonlinear uncertainty infinity Laplacian

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).

Nov 13 - Nov 19, 2022

Nov 6 - Nov 12, 2022

Oct 30 - Nov 5, 2022

The KAUST 2022 Workshop on Statistics

Oct 31, 09:00 - Nov 3, 17:00

B2 B3 A0215

statistics data science big data modeling complex data

The workshop will cover current trends in statistics and the statistical modeling of big and complex data. Over four days, it will have talks by leading experts as well as two poster sessions where students and postdocs will present their work. All talks will be given Auditorium 0215, between Buildings 2 and 3 at KAUST. Speakers Aritz Adin (Public University of Navarre) Ahmadou Alioum (Bordeaux School of Public Health) Denis Allard (INRAE) Vera Baladandayupathani (University of Michigan) Emily Hector (North Carolina State University) Eduardo Garcia Portugues (Carlos III University of Madrid)

Oct 23 - Oct 29, 2022

Oct 16 - Oct 22, 2022

Inference for Longitudinal Data After Adaptive Sampling

Prof. Susan Murphy, Statistics and Computer Science and Radcliffe Alumnae Professor at the Radcliffe Institute, Harvard University

Oct 20, 15:00 - 16:00

B9 L2 H2

Reinforcement Learning

In this work, we proved statistical inference for the common Z-estimator based on adaptively sampled data. Adaptive sampling methods, such as reinforcement learning (RL) and bandit algorithms, are increasingly used for the real-time personalization of interventions in digital applications like mobile health and education. As a result, there is a need to be able to use the resulting adaptively collected user data to address a variety of inferential questions, including questions about time-varying causal effects.
We used Reinforcement Learning; but did it work?

Prof. Susan Murphy, Statistics and Computer Science and Radcliffe Alumnae Professor at the Radcliffe Institute, Harvard University

Oct 19, 16:00 - 17:00

B9 L2 H2

Reinforcement Learning Digital Behavioral Health

Reinforcement Learning provides an attractive suite of online learning methods for personalizing interventions in Digital Behavioral Health. However, after a reinforcement learning algorithm has been run in a clinical study, how do we assess whether personalization occurred? We might find users for whom it appears that the algorithm has indeed learned in which contexts the user is more responsive to a particular intervention. But could this have happened completely by chance? We discuss some first approaches to addressing these questions.
Distributed multi-GPU Algorithms for Hierarchical Matrices

George Turkiyyah, Research Professor, Applied Mathematics and Computational Science

Oct 18, 12:00 - 13:00

B9 L2 R2322

GPU Algorithms hierarchical matrices

In this talk, we show that, besides their optimal O(N) algorithmic complexity, hierarchical matrix operations also benefit from parallel scalability on distributed machines with extremely large core counts. In particular, we describe high-performance, distributed-memory, GPU-accelerated algorithms for matrix-vector multiplication and other operations on hierarchical matrices in the H^2 format.

Oct 9 - Oct 15, 2022

Finite element approximation of parameter dependent eigenvalue problems

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

Oct 11, 12:00 - 13:00

B9 L2 R2322

Finite element approximation of parameter dependent eigenvalue problems

Eigenvalue problems arising from partial differential equations are used to model several applications in science and engineering, ranging from vibrations of structures, industrial microwaves, photonic crystals, and waveguides, to particle accelerators.

Oct 2 - Oct 8, 2022

Sep 25 - Oct 1, 2022

Deep Learning and Scientific Computing

Jinchao Xu, Professor, Applied Mathematics and Computational Science

Sep 27, 12:00 - 13:00

B9 L2 R2322

Deep learning scientific computing

In this talk, I will first give an elementary introduction to basic deep learning models and training algorithms from a scientific computing viewpoint. Using image classification as an example, I will try to give mathematical explanations of why and how some popular deep learning models such as convolutional neural network (CNN) work. Most of the talk will be assessable to an audience who have basic knowledge of calculus and matrix. Toward the end of the talk, I will touch upon some advanced topics to demonstrate the potential of new mathematical insights for helping understand and improve the efficiency of deep learning technologies.

Sep 18 - Sep 24, 2022

Waves Functional Materials: From theory to applications

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

Sep 20, 12:00 - 13:00

B9 L2 R2322

waves Wave functional materials Applications

No abstract is available.

Sep 11 - Sep 17, 2022

Solvers for Coupled Problems in Porous Media Science

Arne Naegel

Sep 13, 12:00 - 13:00

B9 L2 R2322

applied statistics

No abstract is available.

Sep 4 - Sep 10, 2022

Low Rank Everywhere (Almost)

David Keyes, Professor, Applied Mathematics and Computational Science

Sep 6, 12:00 - 13:00

B9 L2 R2322

computational science low rank hierarchical low rank data sparsity

Tile low-rank and hierarchical low-rank matrices can exploit the data sparsity that is discoverable all across computational science. We illustrate in large-scale applications and hybridize with similarly motivated mixed precision representations while featuring ECRC research in progress with many collaborators.

Aug 28 - Sep 3, 2022

Introduction to AMCS / STAT Graduate Seminar 398

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

Aug 30, 12:00 - 13:00

B9 L2 R2322

In this section, prof. Gabriel will be introducing the graduate seminar and its requirements.

Jun 26 - Jul 2, 2022

Better Methods and Theory for Federated Learning: Compression, Client Selection, and Heterogeneity

Samuel Horváth, Ph.D. Student, Statistics

Jun 27, 18:00 - 20:00

B5 L5 R5209

Federated learning Optimization for Machine Learning

Federated learning (FL) is an emerging machine learning paradigm involving multiple clients, e.g., mobile phone devices, with an incentive to collaborate in solving a machine learning problem coordinated by a central server. FL was proposed in 2016 by Konecny et al. and McMahan et al. as a viable privacy-preserving alternative to traditional centralized machine learning since, by construction, the training data points are decentralized and never transferred by the clients to a central server. Therefore, to a certain degree, FL mitigates the privacy risks associated with centralized data collection. Unfortunately, optimization for FL faces several specific issues that centralized optimization usually does not need to handle. In this thesis, we identify several of these challenges and propose new methods and algorithms to address them, with the ultimate goal of enabling practical FL solutions supported with mathematically rigorous guarantees.

Jun 5 - Jun 11, 2022

Joint Posterior Inference for Latent Gaussian Models and extended strategies using INLA

Cristian Chiuchiolo, Ph.D. Student, Electrical and Computer Engineering

Jun 6, 15:00 - 17:00

B3 L5 R5209

Bayesian computational statistics Bayesian Statistics

Bayesian inference is particularly challenging on hierarchical statistical models as computational complexity becomes a significant issue. Sampling-based methods like the popular Markov Chain Monte Carlo (MCMC) can provide accurate solutions, but they likely suffer a high computational burden.

May 29 - Jun 4, 2022

Differentially private testing of symmetry equivalence hypotheses based on the characteristic function

Prof. Simos G. Meintanis, University of Athens

May 29, 15:30 - 16:30

B1 L4 R4102

Differential privacy Neighborhood of model validation Symmetry testing

We investigate privacy aspects of tests for symmetry equivalence null hypotheses. Specifically, we consider weighted L2 type tests as well as chi-squared type tests for multivariate symmetry based on the characteristic function, and their privacy properties are specifically quantified within the context of differential privacy. We consider both the case of known centre as well as tests for symmetry about an unknown centre.

Apr 24 - Apr 30, 2022

Recent approaches for flexible and efficient analysis of non-stationary time series

Prof. Raquel Prado, Department of Statistics, University of California

Apr 28, 16:30 - 17:30

B2 B3 A0215

During the first part of the talk we present an approach that allows for flexible analysis of multivariate non-stationary time series via dynamic models on the partial autocorrelation domain. We discuss various aspects of these models, including the use of shrinkage priors to deal with overfitting issues, as well as hierarchical extensions.
Geometry for computational design and fabrication

Apr 28, 12:00 - 13:00

B9 L2 H2

Geometry plays an important role in the design and fabrication of so-called freeform shapes. This talk will illustrate the fruitful interplay between theory and applications in this area. The focus will be on discrete differential geometry and on applications in architecture and fabrication-aware design.
Time-domain and frequency-domain approaches for multiple and multivariate time series - Lecture 3

Prof. Raquel Prado, Department of Statistics, University of California

Apr 27, 16:00 - 17:30

B1 L4 R4102

In the first part of this lecture we present a review dynamic linear models for multivariate time series and hierarchical dynamic linear models for multiple time series. Topics related to model building as well as closed form, approximate and simulation-based methods for Bayesian filtering, smoothing and forecasting within these classes of models will be discussed.
Non-Gaussian geostatistical models using nearest neighbors processes

Prof. Bruno Sanso, Department of Statistics, University of California

Apr 26, 16:30 - 17:30

B2 B3 A0215

We present a framework for non-Gaussian spatial processes that encompasses large distribution families. Spatial dependence for a set of irregularly scattered locations is described with a mixture of pairwise kernels. Focusing on the nearest neighbors of a given location, within a reference set, we obtain a valid spatial process:
Conditionally Gaussian dynamic models, non-linear models and multi-process models for univariate time series - Lecture 2

Prof. Raquel Prado, Department of Statistics, University of California

Apr 26, 14:00 - 15:30

B1 L4 R3119

We discuss conditionally Gaussian dynamic linear models for analysis and forecasting of univariate time series and present simulation-based methods for Bayesian filtering and smoothing within this class of models, including Markov chain Monte Carlo (MCMC) and sequential Monte Carlo (SMC) methods.
Spatio-Temporal Statistical Models Using Integro-Differential Equations

Prof. Bruno Sanso, Department of Statistics, University of California

Apr 25, 17:45 - 19:00

B1 L4 R4102

We will start by presenting the general framework of Bayesian hierarchical dynamic models (BHDM) for space-time data. Within this framework, we will consider in some detail the special case of linear dynamics. We will review MCMC estimation for conditionally linear dynamic models. We will introduce integro-differential models and give a SPDE justification that provides insights into the connections between the dynamics of the process and the properties of the kernel defining the IDE.
Dynamic linear models: Theory, computation and case studies - Lecture 1

Prof. Raquel Prado, Department of Statistics, University of California

Apr 25, 16:00 - 17:30

B1 L4 R4102

In this lecture we present an overview of dynamic linear models for analysis and forecasting of univariate time series. We will discuss principles for model building and methods for Bayesian filtering, smoothing and forecasting. We will illustrate the use of these models in several case studies arising in different applied areas including environmental sciences and neuroscience.
Model Survival Data with high and ultrahigh dimensional predictors: feature screening and statistical inference - Lecture 2

Yi Li, Professor, Biostatistics, University of Michigan

Apr 24, 16:00 - 18:00

B1 L4 R4102

Continuing on with Lecture 1, this short course introduces various cutting-edge methods that handle survival outcome data with ultrahigh dimensional predictors, that is, when the dimension of predictors is much higher than the sample size. We will also discuss several new methods for quantifying the uncertainty of estimates in a high dimensional survival setting, a very active area in machine learning.

Apr 17 - Apr 23, 2022

Apr 10 - Apr 16, 2022

Apr 3 - Apr 9, 2022

Mar 27 - Apr 2, 2022

ProxSkip: Yes! Local Gradient Steps Provably Lead to Communication Acceleration! Finally!

Peter Richtarik, Professor, Computer Science

Mar 31, 12:00 - 13:00

B9 L2 H2

We introduce ProxSkip a surprisingly simple and provably efficient method for minimizing the sum of a smooth (ƒ) and an expensive nonsmooth proximable (ψ) function.