Events

Nov 2 - Nov 8, 2025

Computational and Statistical Advances in Spatio-Temporal Modeling: Causality, Deep Learning, and High-Performance Computing

Zipei Geng, Ph.D. Student, Statistics

Nov 2, 16:00 - 18:00

B2, L5, R5209

spatio-temporal modeling causality and deep learning high-performance computing

Recent advances in environmental monitoring and remote sensing have led to an unprecedented increase in spatial and spatio-temporal data complexity, presenting both opportunities and challenges for environmental science. This thesis explores three critical challenges in environmental data analysis.

Oct 26 - Nov 1, 2025

On Computation and Robustness Issues in Spatial Statistics

Sihan Chen, Ph.D. Student, Statistics

Oct 30, 14:30 - 16:30

B2, L5, R5220

robust spatial inference computational methods spatial statistics

This thesis develops and evaluates robust statistical methods for the analysis and modeling of spatial data, with a focus on improving inference reliability in the presence of outliers and computational challenges.

Nov 12 - Nov 18, 2023

Family of Mean-Mixtures of Multivariate Normal Distributions

Prof. Narayanaswamy Balakrishnan, Department of Mathematics and Statistics, McMaster University

Nov 12, 15:30 - 16:30

B1 L4 R4102

In this talk, I will describe the family of mean-mixtures of multivariate normal distributions and establish many of its properties, stochastic representations, moments, distributional shape characteristics, etc.
Models and Inference for Non-Gaussian Random Vectors and Fields

Sagnik Mondal, Ph.D. Student, Statistics

Nov 12, 12:30 - 14:30

B5 L5 R5209

The multivariate Gaussian distribution is widely used in many statistical applications due to its appealing features. However, real-world data often violate its assumptions, showing skewness and/or tail-thickness.

Sep 10 - Sep 16, 2023

Goodness-of-fit tests for multivariate skewed distributions based on the characteristic function

Maicon J. Karling, Postdoctoral Research Fellow, Statistics

Sep 14, 12:00 - 13:00

B9 L2 H2

Goodness-of-fit tests determine how well a set of observed data fits a particular probability distribution. They can also show if some categorical variable follows a hypothesized family of distributions.

Jun 4 - Jun 10, 2023

Stochastic environmental modeling in a time of convergence: physics meets artificial intelligence

Prof. Stefano Castruccio, Associate professor, University of Notre Dame, USA

Jun 4, 10:00 - 11:00

B1 L4 R4102

It is widely acknowledged how the relentless surge of Volume, Velocity and Variety of data, as well as the simultaneous increase of computational resources have stimulated the development of data-driven methods with unprecedented flexibility and predictive power. However, not every environmental study entails a large data set: many applications ranging from astronomy or paleo-climatology have a high associated sampling cost and are instead constrained by physics-informed partial differential equations. Throughout the past few years, a new and powerful paradigm has emerged in the machine learning literature, merging data-driven and physics-informed problems, hence providing a unified framework for a whole spectrum of problems ranging from data-rich/context-poor to data-poor/context-rich. In this talk, I will present this new framework and discuss some of the most recent efforts to reformulate it as a stochastic model-based approach, thereby allowing calibrated uncertainty quantification.

Apr 30 - May 6, 2023

Sub-dimensional Mardia measures of multivariate skewness and kurtosis

Joydeep Chowdhury, Postdoctoral Research Fellow, Statistics

May 1, 12:00 - 13:00

B9 L3 R3128

The Mardia measures of multivariate skewness and kurtosis summarize the respective characteristics of a multivariate distribution with two numbers. However, these measures do not reflect the sub-dimensional features of the distribution. Consequently, testing procedures based on these measures may fail to detect skewness or kurtosis present in a sub-dimension of the multivariate distribution. We introduce sub-dimensional Mardia measures of multivariate skewness and kurtosis, and investigate the information they convey about all sub-dimensional distributions of some symmetric and skewed families of multivariate distributions.

Nov 6 - Nov 12, 2022

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.

Feb 20 - Feb 26, 2022

High Performance Statistical Computing (HPSC): Challenges and Best Practices

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

Feb 24, 19:00 - Feb 25, 02:00

KAUST

applied statistics big data

************************************************** Organizers: Sameh Abdulah and Marc Genton ************************************************** Abstract Big data in terms of volume, intensity, and complexity is one of the main challenges to statisticians. The availability of different data sources has essential implications in collecting colossal data volumes that require other ways to be managed and analyzed. Existing analytical tools cannot be applied easily to big data volumes due to memory and computation constraints. Previously, statistical applications and traditional high performance

Jun 6 - Jun 12, 2021

Lagrangian Spatio-Temporal Covariance Functions for Multivariate Nonstationary Random Fields

Mary Lai O. Salvana, Ph.D. Student, Statistics

Jun 7, 17:00 - 19:00

KAUST

In geostatistical analysis, we are faced with the formidable challenge of specifying a valid spatio-temporal covariance function, either directly or through the construction of processes. This task is difficult as these functions should yield positive definite covariance matrices. In recent years, we have seen a flourishing of methods and theories on constructing spatio-temporal covariance functions satisfying the positive definiteness requirement. The current state-of-the-art when modeling environmental processes are those that embed the associated physical laws of the system. The class of Lagrangian spatio-temporal covariance functions fulfills this requirement. Moreover, this class possesses the allure that they turn already established purely spatial covariance functions into spatio-temporal covariance functions by a direct application of the concept of Lagrangian reference frame. In this dissertation, several developments are proposed and new features are provided to this special class.

May 30 - Jun 5, 2021

Stationary and Cyclostationary Processes for Time Series and Spatio-Temporal Data

Soumya Das, Ph.D. Student, Statistics

Jun 1, 16:00 - 18:00

KAUST

Due essentially to the difficulties associated with obtaining explicit forms of stationary marginal distributions of non-linear stationary processes, appropriate characterizations of such processes are worked upon little. After discussing an elaborate motivation behind this thesis and presenting preliminaries in Chapter 1, we characterize, in Chapter 2, the stationary marginal distributions of certain non-linear multivariate stationary processes. To do so, we show that the stationary marginal distributions of these processes belong to specific skew-distribution families, and for a given skew-distribution from the corresponding family, a process, with stationary marginal distribution identical to that given skew-distribution, can be found.

Feb 28 - Mar 6, 2021

On The Coupling between HPC and Statistics: Challenges, Opportunities, and Future Trends of Emerging Techniques -- (Virtual SIAM CSE21 Minisymposium)

Mar 1, 09:00 - 16:00

KAUST

applied statistics big data

Abstract Big data modeling/inference and large-scale simulations have followed largely independent paths to the high-performance computing (HPC) frontier, but important opportunities now arise that can be addressed by combining the strengths of each. HPC is becoming increasingly significant in scaling existing statistical methods to larger and more complex applications and developing novel methods that are amenable to scaling within the constraints that exist in modern HPC architectures. The purpose of this minisymposium is to bring together researchers in the area of statistics and HPC to

Oct 25 - Oct 31, 2020

Spatio-Temporal Statistical Modeling with Application to Wind Energy Assessment in Saudi Arabia

Wanfang Chen, Ph.D., Statistics

Oct 26, 15:00 - 17:00

KAUST

In this thesis, I firstly provide a comprehensive assessment of wind energy resources and associated spatio-temporal patterns over Saudi Arabia in both current and future climate conditions, based on a Regional Climate Model output. A high wind energy potential exists and is likely to persist at least until 2050 over a vast area of Western Saudi Arabia, particularly in the region between Medina and the Red Sea coast and during Summer months. Since an accurate assessment of wind extremes is crucial for risk management purposes, I then present the first high-resolution risk assessment of wind extremes over Saudi Arabia.

Apr 5 - Apr 11, 2020

Computation of High-Dimensional Multivariate Normal and Student-t Probabilities Based on Matrix Compression Schemes

Jian Cao, Ph.D., Statistics

Apr 6, 16:00 - 18:00

KAUST

The thesis focuses on the computation of high-dimensional multivariate normal (MVN) and multivariate Student-t (MVT) probabilities. Firstly, a generalization of the conditioning method for MVN probabilities is proposed and combined with the hierarchical matrix representation. Next, I revisit the Quasi-Monte Carlo (QMC) method and improve the state-of-the-art QMC method for MVN probabilities with block reordering, resulting in a ten-time-speed improvement. The thesis proceeds to discuss a novel matrix compression scheme using Kronecker products. This novel matrix compression method has a memory footprint smaller than the hierarchical matrices by more than one order of magnitude. A Cholesky factorization algorithm is correspondingly designed and shown to accomplish the factorization in 1 million dimensions within 600 seconds. To make the computational methods for MVN probabilities more accessible, I introduce an R package that implements the methods developed in this thesis and show that the package is currently the most scalable package for computing MVN probabilities in R. Finally, as an application, I derive the posterior properties of the probit Gaussian random field and show that the R package I introduce makes the model selection and posterior prediction feasible in high dimensions.

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.

Nov 24 - Nov 30, 2019

Asymptotically Exact Variational Bayes for High-Dimensional Probit Models

Prof. Daniele Durante, Department of Decision Sciences, Bocconi University, Italy

Nov 27, 15:30 - 16:30

B1 L4 R4102

Bayesian approach to inference decision sciences

Abstract There are several Bayesian models where the posterior density is not available in a closed and tractable form. In these situations, Markov chain Monte Carlo algorithms and approximate methods, such as variational Bayes and expectation-propagation, provide common solutions to perform posterior inference. However, in high-dimensional studies, with large or even huge p, these approaches still face open problems in terms of scalability and quality of the posterior approximation. Notably, such issues also arise in basic predictor-dependent models for binary data which appear in a wide

Nov 17 - Nov 23, 2019

2019 Statistics and Data Science Workshop

Nov 18, 00:00 - Nov 20, 23:45

B2 B3 A0215

2019 Statistics and Data Science Workshop confirmed speakers include Prof. Alexander Aue, University of California Davis, USA, Prof. Francois Bachoc, University Toulouse 3, France, Prof. Rosa M. Crujeiras Casais, University of Santiago de Compostela, Spain, Prof. Emanuele Giorgi, Lancaster University, UK, Prof. Jeremy Heng, ESSEC Asia-Pacific, Singapore, Prof. Birgir Hrafnkelsson, University of Iceland, Iceland, Prof. Ajay Jasra, KAUST, Saudi Arabia, Prof. Emtiyaz Khan, RIKEN Center for Advanced Intelligence Project, Japan, Prof. Robert Krafty, University of Pittsburgh, USA, Prof. Guido Kuersteiner, University of Maryland, USA, Prof. Paula Moraga, University of Bath, UK, Prof. Tadeusz Patzek, KAUST, Saudi Arabia, Prof. Brian Reich, North Carolina State University, USA, Prof. Dag Tjostheim, University Bergen, Norway, Prof. Xiangliang Zhang, KAUST, Saudi Arabia, Sylvia Rose Esterby, University of British Colombia, Canada, Prof. Abdel El-Shaarawi, Retired Professor at the National Water Research Institute, Canada. View Workshop schedule and abstracts here.

Nov 10 - Nov 16, 2019

Mar 31 - Apr 6, 2019

High-dimensional Q-learning for Dynamic Treatment Regimes

Prof. Rui Song, Department of Statistics, North Carolina State University

Mar 31, 12:00 - 13:00

B3 L5 R5220

HQ-learning statistical inference data analysis simulation

Dynamic treatment regimes are a set of decision rules and each treatment decision is tailored over time according to patients’ responses to previous treatments as well as covariate history. There is a growing interest in development of correct statistical inference for optimal dynamic treatment regimes to handle the challenges of nonregularity problems in the presence of nonrespondents who have zero-treatment effects, especially when the dimension of the tailoring variables is high.

Oct 4 - Oct 10, 2015

Workshop on Computational Space-Time Statistics

Oct 4, 09:45 - Oct 6, 10:45

B1 L4 R4102

Statistics of extremes Environmental Statistics

Workshop on Computational Space-Time Statistics