Monday, June 07, 2021, 17:00
- 19:00
https://kaust.zoom.us/j/4140228838
Contact Person
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.
Tuesday, June 01, 2021, 16:00
- 18:00
https://kaust.zoom.us/j/96960741449
Contact Person
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.
Monday, May 31, 2021, 16:00
- 18:00
https://kaust.zoom.us/j/93176117006
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The modeling of spatio-temporal and multivariate spatial random fields has been an important and growing area of research due to the increasing availability of space-time-referenced data in a large number of scientific applications. In geostatistics, the covariance function plays a crucial role in describing the spatio-temporal dependence in the data and is key to statistical modeling, inference, stochastic simulation, and prediction. Therefore, the development of flexible covariance models, which can accommodate the inherent variability of the real data, is necessary for advantageous modeling of random fields. This thesis is composed of four significant contributions in the development and applications of new covariance models for stationary multivariate spatial processes, and nonstationary spatial and spatio-temporal processes. Firstly, this thesis proposes a semiparametric approach for multivariate covariance function estimation with flexible specification of the cross-covariance functions via their spectral representations. The flexibility in the proposed cross-covariance function arises due to B-spline based specification of the underlying coherence functions, which in turn allows for capturing non-trivial cross-spectral features. The proposed method is applied to model and predict the bivariate data of particulate matter concentration and wind speed in the United States. Secondly, this thesis introduces a parametric class of multivariate covariance functions with asymmetric cross-covariance functions. The proposed covariance model is applied to analyze the asymmetry and perform prediction in a trivariate data of particulate matter concentration, wind speed and relative humidity in the United States.
 Thirdly, the thesis presents a space deformation method which imparts nonstationarity to any stationary covariance function. The proposed method utilizes the functional data registration algorithm and classical multidimensional scaling to estimate the spatial deformation. The application of the proposed method is demonstrated on precipitation data from Colorado, United States. Finally, this thesis proposes a parametric class of time-varying spatio-temporal covariance functions, which are stationary in space but nonstationary in time. The proposed time-varying spatio-temporal covariance model is applied to study the seasonality effect and perform space-time predictions in the daily particulate matter concentration data from Oregon, United States.
Georgiy L. Stenchikov, Professor, Earth Science and Engineering
Thursday, April 29, 2021, 12:00
- 13:00
https://kaust.zoom.us/j/94262797011?pwd=ZXBBcnltQ3JvZkdhWFZjTEptL3FmUT09
Explosive volcanic eruptions are magnificent events that in many ways affect the Earth’s natural processes and climate. They cause sporadic perturbations of the planet’s energy balance, activating complex climate feedbacks and providing unique opportunities to better quantify those processes. We know that explosive eruptions cause cooling in the atmosphere for a few years, but we have just recently realized that they affect the major climate variability modes and volcanic signals can be seen in the subsurface ocean for decades. The volcanic forcing of the previous two centuries offsets the ocean heat uptake and diminishes global warming by about 30%. In the future, explosive volcanism could slightly delay the pace of global warming and has to be accounted for in long-term climate predictions. The recent interest in dynamic, microphysical, chemical and climate impacts of volcanic eruptions is also excited by the fact these impacts provide a natural analog for climate geoengineering schemes involving the deliberate development of an artificial aerosol layer in the lower stratosphere to counteract global warming. In this talk, I will discuss these recently discovered volcanic effects and specifically pay attention to how we can learn about the hidden Earth-system mechanisms activated by explosive volcanic eruptions.
Prof. Peter Diggle, Statistics in the faculty of Health and Medicine, Lancaster University
Tuesday, April 27, 2021, 15:00
- 16:30
https://kaust.zoom.us/j/97813381559
Contact Person

In low-resource settings, disease registries do not exist, and prevalence mapping relies on data collected form surveys of disease prevalence taken in a sample of the communities at risk within the region of interest, possibly supplemented by remotely sensed images that can act as proxies for environmental risk factors. A standard geostatistical model for data of this kind is a generalized linear mixed model, Yᵢ ~ Binomial(mᵢ; P(xᵢ)) log [P(x)/{(1- P(xᵢ)}] = d(x)β + S(x), where Yᵢ is the number of positives in a sample of mi individuals at location xᵢ, d(x) is a vector of spatially referenced explanatory variables available at any location x within the region of interest, and S(x) is a Gaussian process.

In this talk, I will first review statistical methods and software associated with this standard model, then consider several methodological extensions and their applications to some Africa-wide control programmes for Neglected Tropical Diseases to demonstrate the very substantial gains in efficiency that can be obtained by comparison with currently used methods.

Thursday, April 22, 2021, 12:00
- 13:00
https://kaust.zoom.us/j/94262797011?pwd=ZXBBcnltQ3JvZkdhWFZjTEptL3FmUT09
We develop several new communication-efficient second-order methods for distributed optimization. Our first method, NEWTON-STAR, is a variant of Newton's method from which it inherits its fast local quadratic rate. However, unlike Newton's method, NEWTON-STAR enjoys the same per iteration communication cost as gradient descent. While this method is impractical as it relies on the use of certain unknown parameters characterizing the Hessian of the objective function at the optimum, it serves as the starting point which enables us to design practical variants thereof with strong theoretical guarantees. In particular, we design a stochastic sparsification strategy for learning the unknown parameters in an iterative fashion in a communication efficient manner. Applying this strategy to NEWTON-STAR leads to our next method, NEWTON-LEARN, for which we prove local linear and superlinear rates independent of the condition number. When applicable, this method can have dramatically superior convergence behavior when compared to state-of-the-art methods. Finally, we develop a globalization strategy using cubic regularization which leads to our next method, CUBIC-NEWTON-LEARN, for which we prove global sublinear and linear convergence rates, and a fast superlinear rate. Our results are supported with experimental results on real datasets, and show several orders of magnitude improvement on baseline and state-of-the-art methods in terms of communication complexity.
Thursday, April 08, 2021, 12:00
- 13:00
https://kaust.zoom.us/j/94262797011?pwd=ZXBBcnltQ3JvZkdhWFZjTEptL3FmUT09
COVID-19 has caused a global pandemic and become the most urgent threat to the entire world. Tremendous efforts and resources have been invested in developing diagnosis. Despite the various, urgent advances in developing artificial intelligence (AI)-based computer-aided systems for CT-based COVID-19 diagnosis, most of the existing methods can only perform classification, whereas the state-of-the-art segmentation method requires a high level of human intervention. In this talk, I will introduce our recent work on a fully-automatic, rapid, accurate, and machine-agnostic method that can segment and quantify the infection regions on CT scans from different sources. Our method is founded upon three innovations: 1) an embedding method that projects any arbitrary CT scan to a same, standard space, so that the trained model becomes robust and generalizable; 2) the first CT scan simulator for COVID-19, by fitting the dynamic change of real patients’ data measured at different time points, which greatly alleviates the data scarcity issue; and 3) a novel deep learning algorithm to solve the large-scene-small-object problem, which decomposes the 3D segmentation problem into three 2D ones, and thus reduces the model complexity by an order of magnitude and, at the same time, significantly improves the segmentation accuracy. Comprehensive experimental results over multi-country, multi-hospital, and multi-machine datasets demonstrate the superior performance of our method over the existing ones and suggest its important application value in combating the disease.
Thursday, April 01, 2021, 12:00
- 13:00
https://kaust.zoom.us/j/94262797011?pwd=ZXBBcnltQ3JvZkdhWFZjTEptL3FmUT09
Wave functional materials are artificial materials that can control wave propagation as wish. In this talk, I will give a brief review on the progress of wave functional materials and reveal the secret behind the engineering of these materials to achieve desired properties. In particular, I will focus on our contributions on metamaterials and metasurfaces. I will introduce the development of effective medium, a powerful tool in modeling wave functional materials, followed by some illustrative examples demonstrating the intriguing properties, such as redirection, emission rate enhancement, wave steering and cloaking.
Thursday, March 25, 2021, 12:00
- 13:00
https://kaust.zoom.us/j/94262797011?pwd=ZXBBcnltQ3JvZkdhWFZjTEptL3FmUT09
In modern large-scale inference problems, the dimension of the signal to be estimated is comparable or even larger than the number of available observations. Yet the signal of interest lies in some low-dimensional structure, due to sparsity, low-rankness, finite alphabet, ... etc. Non-smooth regularized convex optimization are powerful tools for the recovery of such structured signals from noisy linear measurements. Research has shifted recently to the performance analysis of these optimization tools and optimal turning of their hyper-parameters in high dimensional settings. One powerful performance analysis framework is the Convex Gaussian Min-max Theorem (CGMT). The CGMT is based on Gaussian process methods and is a strong and tight version of the classical Gordon comparison inequality. In this talk, we review the CGMT and illustrate its application to the error analysis of some convex regularized optimization problems.
Thursday, March 11, 2021, 12:00
- 13:00
https://kaust.zoom.us/j/94262797011?pwd=ZXBBcnltQ3JvZkdhWFZjTEptL3FmUT09
Small-scale cut and fold patterns imposed on sheet material enable its morphing into three-dimensional shapes. This manufacturing paradigm has received much attention in recent years and poses challenges in both fabrication and computation. It is intimately connected with the interpretation of patterned sheets as mechanical metamaterials, typically of negative Poisson ratio. We discuss a fundamental geometric question, namely the targeted programming of a shape morph from a flat sheet to a curved surface, or even between any two shapes. The solution draws on differential geometry, discrete differential geometry, geometry processing and geometric optimization.
Dr. Ewelina Zatorska, Senior Lecturer in the Applied and Numerical Analysis, Imperial College London
Tuesday, March 09, 2021, 15:00
- 16:00
https://kaust.zoom.us/j/92756762717
In this talk, I will present the recent developments in the topic of the existence of solutions to the two-fluid systems. The compensated compactness technique of P.-L. Lions and E. Feireisl for single-component fluids has certain limitations, distinctly in the context of multi-component flow models. A particular example of such a model is the two-fluids Stokes system with a single velocity field and two densities, and with an algebraic pressure law closure. The first result that I will present is the existence of weak solutions for such a system, using the compactness criterion introduced recently by D. Bresch and P.-E. Jabin. I will also outline an innovative construction of solutions relying on the G. Crippa and C. DeLellis stability estimates for the transport equation. In the last part of my talk, I will relate to a couple of more recent results: the existence of solutions to the one-dimensional system, non-uniqueness of solutions to the inviscid system, and I will comment on issues around weak-strong uniqueness.
Thursday, February 25, 2021, 12:00
- 13:00
https://kaust.zoom.us/j/94262797011?pwd=ZXBBcnltQ3JvZkdhWFZjTEptL3FmUT09
Contact Person

Abstract

The outstanding performance of deep neural networks (DNNs), for visual recognition tasks

Thursday, February 18, 2021, 12:00
- 13:00
https://kaust.zoom.us/j/94262797011?pwd=ZXBBcnltQ3JvZkdhWFZjTEptL3FmUT09
Contact Person
Machine learning has been widely applied to diverse problems of forwarding prediction and backward design. The former problem of forwarding prediction is to predict the reaction of a system given the input x, i.e., y=f(x). This talk will introduce several groups of algorithms for learning the prediction function f. The backward design is an inverse problem, predicting the input x according to the system reaction y, i.e., x=g(y). This is an important problem for the design of chemical material and optical devices. This talk will introduce several successful application examples of machine learning algorithms on the backward design problems.
Dr. Boris Beranger, Lecturer in Statistics, University of New South Wales, Sydney
Tuesday, February 16, 2021, 11:00
- 12:00
https://kaust.zoom.us/j/94182664982
Contact Person
Droughts, high temperatures and strong winds are key causes of the recent bushfires that have touched a major part of the Australian territory. Such extreme events seem to appear with increasing frequency, creating an urgent need to better understand the behavior of extreme environmental phenomena.
Thursday, February 11, 2021, 12:00
- 13:00
https://kaust.zoom.us/j/94262797011?pwd=ZXBBcnltQ3JvZkdhWFZjTEptL3FmUT09
Contact Person
In this talk, we begin by a brief introduction to proper scoring rules and their use in statistics. Then, we discuss an often overlooked problem: the up-weighting of observations with large uncertainty, which can lead to unintuitive rankings of models, by some of the most popular proper scoring rules, such as the continuously ranked probability score (CRPS), the MAE, and the MSE.
Antik Chakraborty, Postdoc, Department of Statistical Science, Duke University
Sunday, February 07, 2021, 16:00
- 17:00
https://kaust.zoom.us/j/98635976499
Contact Person
A healthy ecosystem is essential to our well being and to ensure that we need a better understanding of the life or biodiversity around us. In the first part of this talk, I will introduce how modern technology is being used to collect data on biodiversity across space and time. These data come in complex forms such as DNA sequences, high-dimensional binary vectors, sound signals, etc. I will briefly touch upon the statistical challenges involved in making sense of these data.   In the second part, I will elaborate on a project motivated by studying temporal patterns in bird vocalizations. I will introduce a new class of semiparametric latent variable models for long memory discretized event data. The proposed class of FRActional Probit (FRAP) models are based on the thresholding of a latent process consisting of an additive expansion of a smooth Gaussian process with a fractional Brownian motion. I will describe a Bayesian approach to inference using Markov chain Monte Carlo. Results from applying the model on Amazon bird vocalization data will be presented which provide substantial evidence for self-similarity and non-Markovian/Poisson dynamics. A hierarchical extension of the proposed model to accommodate vocalizations of multiple birds at the same time will also be discussed.
Nevena Tomic, Library Subject Specialist, PSE, KAUST
Thursday, February 04, 2021, 12:00
- 13:00
https://kaust.zoom.us/j/94262797011?pwd=ZXBBcnltQ3JvZkdhWFZjTEptL3FmUT09
This session will help you to improve the efficiency of your literature searches. You will learn basic search techniques – using Boolean operators, keyword search, concept search, truncation, etc. Two major interdisciplinary scientific databases – Web of Science and Scopus, as well as CEMSE specific resources in KAUST library collections will be presented.
Tony Chan, President, King Abdullah University of Science and Technology
Thursday, January 28, 2021, 12:00
- 13:00
https://kaust.zoom.us/j/94262797011?pwd=ZXBBcnltQ3JvZkdhWFZjTEptL3FmUT09
Computational mathematics has a millennium long history but its modern incarnation started after the advent of electronic computers about 80 years ago. Scientifically, it lies in the intersection between mathematics, a subject with a long history, and computer sciences, a relatively new discipline. Its motivations, approaches and practitioners have derived from different fields, and it has also had to evolve and adapt to new tools and opportunities. My own scientific career overlaps quite a bit with the field’s modern evolution and in this talk, I’ll give a personal, as well as a “historical” view of the field.
Thursday, December 10, 2020, 12:00
- 13:00
https://kaust.zoom.us/j/95474758108?pwd=WkwrdiszTE1uYTdmR3JRK09LVDErZz09
Contact Person
Geospatial health data are essential to inform public health and policy. These data can be used to quantify disease burden, understand geographic and temporal patterns, identify risk factors, and measure inequalities. In this talk, I will give an overview of my research which focuses on the development of geospatial methods and interactive visualization applications for health surveillance. I will present disease risk models where environmental, demographic and climatic data are used to predict the risk and identify targets for intervention of lymphatic filariasis in sub-Saharan Africa, and leptospirosis in a Brazilian urban slum. I will also show the R packages epiflows for risk assessment of travel-related spread of disease, and SpatialEpiApp for disease mapping and the detection of clusters. Finally, I will describe my future research and how it can inform better surveillance and improve population health globally.
Thursday, December 03, 2020, 12:00
- 13:00
https://kaust.zoom.us/j/95474758108?pwd=WkwrdiszTE1uYTdmR3JRK09LVDErZz09
Biological systems are distinguished by their enormous complexity and variability. That is why mathematical modeling 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 modeling 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. Moreover, the models of the processes running on these geometries are not yet well established, since these are equally complex and often couple many scales in space and time. Thus, simulating such systems always means to put the whole frame to test, from modelling to the numerical methods and software tools used for simulation. These need to be advanced in connection with validating simulation results by comparing them to experiments.
Carlos Cinelli, Ph.D. candidate, Department of Statistics, UCLA
Monday, November 30, 2020, 16:30
- 17:30
https://kaust.zoom.us/j/96131500026
Contact Person
The past few decades have witnessed rapid and unprecedented theoretical progress in the science of causal inference, ranging from the “credibility revolution” with the popularization of quasi-experimental designs, to the development of a complete solution to non-parametric identification with causal graphical models. Most of these theoretical progress, however, relies on strong, exact assumptions, such as the absence of unobserved common causes, or the absence of certain direct effects. Unfortunately, more often than not these assumptions are very hard to defend in practice. This leads to two undesirable consequences for applied quantitative work in the data-intensive sciences: (i) important research questions may be neglected, simply because they do not exactly match the requirements of current methods; or, (ii) researchers may succumb to making the required “identification assumptions” simply to justify the use of available methods, but not because these assumptions are truly believed (or understood).  In this talk, I will discuss new theories, methods, and software for permitting causal inferences under more flexible and realistic settings. These tools empower scientists, and policymakers to both examine the sensitivity of causal inferences to violations of its underlying assumptions, and also to draw robust and trustworthy conclusions from settings in which traditional methods fail.  
Tuesday, November 24, 2020, 09:00
- 10:00
https://kaust.zoom.us/j/98560746589
Contact Person
Intrinsic connectivity networks (ICNs) refer to brain functional networks that are consistently found under various conditions, during tasks or at rest. Some studies demonstrated that while some stimuli do not impact intrinsic connectivity, other stimuli actually activate intrinsic connectivity through suppression, excitation, moderation or modification. Most analyses of fMRI data use ad-hoc methods to estimate the latent structure of ICNs. Modeling the effects on ICNs has also not been fully investigated. We propose a Bayesian Intrinsic Connectivity Network (BICNet) model, an extended Bayesian dynamic sparse latent factor model, to identify the ICNs and quantify task-related effects on the ICNs. BICNet has the following advantages: (1) It simultaneously identifies the individual and group-level ICNs; (2) It robustly identifies ICNs by jointly modeling rfMRI and tfMRI; (3) Compared to ICA-based methods, it can quantify the difference of ICN amplitudes across different states; (4) The sparsity of ICNs automatically performs feature selection, instead of ad-hoc thresholding. We apply BICNet to the rfMRI and language tfMRI data from the HCP and identify several ICNs related to distinct language processing functions.
Thursday, November 19, 2020, 12:00
- 13:00
https://kaust.zoom.us/j/95474758108?pwd=WkwrdiszTE1uYTdmR3JRK09LVDErZz09
In this talk we consider the problem of estimating the score function (or gradient of the log-likelihood) associated to a class of partially observed diffusion processes, with discretely observed, fixed length, data and finite dimensional parameters. We construct an estimator that is unbiased with no time-discretization bias. Using a simple Girsanov change of measure method to represent the score function, our methodology can be used for a wide class of diffusion processes and requires only access to a time-discretization method such as Euler-Maruyama. Our approach is based upon a novel adaptation of the randomization schemes developed by Glynn and co-authors along with a new coupled Markov chain simulation scheme. The latter methodology is an original type of coupling of the coupled conditional particle filter. We prove that our estimator is unbiased and of finite variance. We then illustrate our methodology on several challenging statistical examples. This is a joint work with Jeremy Heng (ESSEC, Singapore) and Jeremie Houssineau (Warwick, UK)