Events

Feb 27 - Mar 5, 2022

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  Extensions of Dynamic Programming for Combinatorial Optimization and Data Mining - 2022-03-03

  Mikhail Moshkov, Professor, Applied Mathematics and Computational Science

  Mar 3, 12:00 - 13:00

  B9 L2 H2

  Dynamic programming is an efficient technique to solve optimization problems. It is based on decomposing the initial problem into simpler ones and solving these sub-problems beginning from the simplest ones. A conventional dynamic programming algorithm returns an optimal object from a given set of objects. We developed extensions of dynamic programming which allow us (i) to describe the set of objects under consideration, (ii) to perform a multi-stage optimization of objects relative to different criteria, (iii) to count the number of optimal objects, (iv) to find the set of Pareto optimal points for the bi-criteria optimization problem, and (v) to study the relationships between two criteria. The considered applications include optimization of decision trees and decision rule systems as algorithms for problem-solving, as ways for knowledge representation, and as classifiers, optimization of element partition trees for rectangular meshes which are used in finite element methods for solving PDEs, and multi-stage optimization for such classic combinatorial optimization problems as matrix chain multiplication, binary search trees, global sequence alignment, and shortest paths.

Feb 20 - Feb 26, 2022

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  Combined analysis of spatially misaligned data using Gaussian fields and the stochastic partial differential equation approach

  Feb 24, 12:00 - 13:00

  B9 L2 H2

  Spatially misaligned data are becoming increasingly common due to advances in both data collection and management in a wide range of scientific disciplines including the epidemiological, ecological and environmental fields. Here, we present a Bayesian geostatistical model for fusion of data obtained at point and areal resolutions. The model assumes that underlying all observations there is a spatially continuous variable that can be modeled using a Gaussian random field process.
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  Extreme Statistics: Theory and Methods for Modeling Rare High-Impact Events in Complex Settings

  Raphaël Huser, Associate Professor, Statistics

  Feb 20, 16:00 - 17:30

  B4 B5 A0215

  Rare, low-probability events often lead to the biggest impacts. The goal of the Extreme Statistics (extSTAT) research group at KAUST is to develop cutting-edge statistical approaches for modeling, predicting and quantifying risks associated with these extreme events in complex systems arising in various scientific fields, such as climate science and finance. In particular, the work that we develop and continue to refine has a direct potential impact to climate scientists and related stakeholders, such as engineers and insurers, who have realized that under climate change, the greatest environmental, ecological, and infrastructural risks and damages, are often caused by changes in the intensity, frequency, spatial extent, and persistence of extreme events, rather than changes in their average behavior. However, while datasets are often massive in modern day applications, extreme events are always scarce by nature. This makes it very challenging to provide reliable risk assessment and prediction, especially when extrapolation to yet-unseen levels is required. To overcome these existing limitations, the extSTAT research group develops novel methods that transcend classical extreme-value theory to build new resilient statistical models, as well as computationally efficient inference methods, which improve the prediction of rare events in complex, high-dimensional, spatio-temporal, non-stationary settings. In this talk, I will provide an overview of my group's recent research activities and future directions with a focus on core statistical methodology contributions. The technical part of the talk will describe selected research highlights, which include (but are not limited to) the development of new flexible sub-asymptotic models applied to assessing contagion risk among leading cryptocurrencies, the development of a novel low-rank spatial modeling framework applied to estimating extreme hotspots in high-resolution Red Sea surface temperature data, and the development specialized spatio-temporal point process models applied to predicting devastating rainfall-induced landslides in a region of Italy. I will conclude my talk with an outlook on my future research plans. Motivated by methodological obstacles that arise with “big models” for complex extremes data, as well as new substantive challenges in collaborative work at KAUST, we will embark on the development of fundamentally superior models that have an intrinsically sparse probabilistic structure, as well as new "hybrid" methods that combine the strength of (parametric) models from extreme-value theory with the pragmatism and predictive power of (nonparametric) machine learning algorithms, thus opening the door to interpretable and “extreme-ly” accurate predictive models for rare events in unprecedented dimensions.

Feb 13 - Feb 19, 2022

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  Modelling and Simulation of Biological Systems - 2022-02-17

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

  Feb 17, 12:00 - 13:00

  B9 L2 H2

  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.
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  Causal reasoning in settings with competing events and truncation by death

  Prof. Mats Julius Stensrud, Department of Mathematics, EPFL

  Feb 16, 16:00 - 17:00

  B9 L2 R2322

  A competing event is any event that makes it impossible for the outcome of interest to occur. The presence of competing events requires us to be careful about the interpretation of classical causal estimands. In particular, the average treatment effect captures effects through the competing event, pathways that may not be of primary interest. As a solution, we suggest the separable effect, inspired by Robins and Richardson’s extended graphical approach. We will give criteria that allow different interpretations of the separable effects and present identification conditions that can be evaluated in causal graphs.

Feb 6 - Feb 12, 2022

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  Exploring Spectral Dependence in Multivariate Time Series

  Hernando Ombao, Professor, Statistics

  Feb 10, 12:00 - 13:00

  KAUST

  Advances in imaging technology have given neuroscientists unprecedented access to examine various facets of how the brain “works”. Brain activity is complex. A full understanding of brain activity requires careful study of its multi-scale spatial-temporal organization (from neurons to regions of interest; and from transient events to long-term temporal dynamics). Motivated by these challenges, we will explore some characterizations of dependence between components of a multivariate time series and then apply these to the study of brain functional connectivity. This is potentially interesting for brain scientists because functional brain networks are associated with cognitive function and mental and neurological diseases.

Jan 30 - Feb 5, 2022

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  A Non-stationary Autologistic Model for Space-time Data

  Yiping Hong, Postdoctoral Research Fellow, Statistics

  Feb 3, 12:00 - 13:00

  KAUST

  Abstract In many research fields such as meteorology, ecology, and epidemiology, the spatio-temporal datasets are binary, describing the existence of particular species or events. The spatial dependence of the binary observations often exhibits nonstationarity. However, most nonstationary models in the literature are based on Gaussian random fields, which may be computational demanding. We propose a nonstationary spatio-temporal autologistic regression model, which allows the spatial covariances to vary in space. We investigate the spatial and temporal correlation of autologistic models with
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  Exoplanet Imaging Coronagraphic Mask Development and Testing: A North American Perspective

  Benjamin L. Gerard, Postdoctoral Scholar, University of California, UC Observatories

  Feb 2, 16:30 - 17:30

  KAUST

  Over 4000 exoplanets - planets beyond the Solar System - have been discovered since the first Nobel prize-winning exoplanet detection around a Sun-like star in 1995. The majority of these exoplanets have been detected by indirect methods, inferring the presence of the exoplanet by observing the star.

Jan 23 - Jan 29, 2022

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  Backward induction: from theory to empirics

  Konrad Grabiszewski, Instructional Professor, Applied Mathematics and Computational Sciences

  Jan 27, 12:00 - 13:00

  KAUST

  Backward induction, the cornerstone of dynamic game theory, is the classical algorithm applied to solve finite dynamic games with perfect and complete information. While theoretically sound and beautiful in its simplicity, backward induction does not perform so well when it comes to predicting human behavior. The objective of this seminar is twofold. First, we will understand what backward induction is and how to apply it on game-theoretic trees. Second, we will answer the question of whether backward induction is a good model of how people make choices in dynamic interactions.

Dec 5 - Dec 11, 2021

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  Backward Error Analysis for Nonlinear Differential Equations

  David Ketcheson, Professor, Applied Mathematics and Computational Science

  Dec 9, 12:00 - 13:00

  KAUST

  Backward error analysis is a tool that allows us to understand the effect of errors without knowing exact solutions.
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  Deconfounding

  Professor Peter Bühlmann, Statistics and Mathematics, ETH Zürich

  Dec 7, 15:30 - 16:30

  B9 L2 H2

  Hidden confounding is a severe problem when interpreting regression or causal parameters, and it may also lead to poor generalization performance for prediction. Adjusting for unobserved confounding is important but challenging when based on observational data only.
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  Statistical Learning: Causal-oriented and Robust

  Professor Peter Bühlmann, Statistics and Mathematics, ETH Zürich

  Dec 7, 12:00 - 13:00

  B9 L2 H2

  Reliable, robust and interpretable machine learning is a big emerging theme in data science and artificial intelligence, complementing the development of pure black box prediction algorithms. Looking through the lens of statistical causality and exploiting a probabilistic invariance property opens up new paths and opportunities for enhanced interpretation, robustness and external validity, with wide-ranging prospects for various applications.

Nov 28 - Dec 4, 2021

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  Particle filter in high dimensions

  Dec 2, 12:00 - 13:00

  KAUST

  Filtering (or data assimilation) is the process of combining noisy observational data with an output of a numerical model to produce an optimal estimate of the evolving state of the system.
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  Semiparametric Regression Models for Between and Within-subject Attributes: Applications to High-Dimensional Data, Asymptotic Efficiency and Beyond

  Jinyuan Liu, PhD Student, Biostatistics, University of California, USA

  Nov 28, 08:30 - 09:30

  KAUST

  Breakthroughs such as high-throughput sequencing are generating flourishing high-dimensional data that provoke challenges in both statistical analyses and interpretations.

Nov 21 - Nov 27, 2021

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  An efficient high-order reconstructions multidimensional scheme with application to instabilities in direct initiation of gaseous detonations in free space

  Matteo Parsani, Associate Professor, Applied Mathematics and Computational Science

  Nov 25, 12:00 - 13:00

  KAUST

  When constructing high-order schemes for solving hyperbolic conservation laws with multi-dimensional finite volume schemes, the corresponding high-order reconstructions are commonly performed in characteristic spaces to eliminate spurious oscillations as much as possible.

Nov 14 - Nov 20, 2021

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  Forecasting High-Frequency Spatio-Temporal Wind Power with Dimensionally Reduced Echo State Networks

  Huang Huang, Ph.D., Statistics

  Nov 18, 12:00 - 13:00

  KAUST

  Fast and accurate hourly forecasts of wind speed and power are crucial in quantifying and planning the energy budget in the electric grid.

Nov 7 - Nov 13, 2021

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  Compatibility conditions for finite element approximations

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

  Nov 11, 12:00 - 13:00

  KAUST

  In the classical theory of the finite element approximation of elliptic partial differential equations, based on standard Galerkin schemes, the energy norm of the error decays with the same rate of convergence as the best finite element approximation, without any additional requirements on the involved spaces.
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  Joint Quantile Disease Mapping for Areal Data

  Hanan Alahmadi, Ph.D. Student, Statistics

  Nov 7, 15:00 - 16:00

  B1 L4 R4102

  The statistical analysis based on the quantile method is more comprehensive, flexible, and not sensitive against outliers compared to the mean methods. The study of the joint disease mapping focuses on the mean regression. This means they study the correlation or the dependence between the means of the diseases by using standard regression. However, sometimes one disease limits the occurrence of another disease. In this case, the dependence between the two diseases will not be in the means but in the different quantiles; thus, the analyzes will consider a joint disease mapping of high quantile for one disease with low quantile of the other disease.

Oct 31 - Nov 6, 2021

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  Joint modeling of dependent diverse format data: A review from several case-studies

  Giovani Silva, PhD in Mathematics (IST) at the University of Lisbon

  Nov 4, 12:00 - 13:00

  KAUST

  his talk reviews some fundamental and practical issues related to the formulation and analysis of joint models of mixed types of outcomes with latent variables, with particular emphasis on both several case-studies in applied statistics and their computational implementation

Oct 24 - Oct 30, 2021

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  Recent progress on symbolic computation for PDE and their numerical schemes

  Diogo Gomes, Professor, Applied Mathematics and Computational Science

  Oct 28, 12:00 - 13:00

  KAUST

  The qualitative study of PDEs often relies on integral identities and inequalities. For example, for time-dependent PDEs, conserved integral quantities or quantities that are dissipated play an important role. In particular, if these integral quantities have a definite sign, they are of great interest as they may provide control on the solutions to establish well-posedness.

Oct 17 - Oct 23, 2021

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  Enabling Accurate and Efficient Simulation in Visual Computing - Graduate Seminar - AMCS - STAT

  Dominik L. Michels, Associate Professor, Computer Science

  Oct 21, 12:00 - 13:00

  KAUST

  The overarching goal of Prof. Michels' Computational Sciences Group within KAUST's Visual Computing Center is enabling accurate and efficient simulations for applications in Scientific and Visual Computing. Towards this goal, the group develops new principled computational methods based on solid theoretical foundations.

Oct 10 - Oct 16, 2021

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  Bayesian image analysis in Fourier space (BIFS) models and some relationships with Markov random fields

  Prof. John Kornak, Biostatistics, University of California, San Francisco

  Oct 14, 16:30 - 17:45

  B4 B5 A0215

  bayesian analysis

  Abstract For over 30 years, Bayesian image analysis has provided an important pathway to image reconstruction and enhancement, by balancing a priori expectations of image characteristics with a model for the noise process. The conventional Bayesian modeling approach defined in image space implements priors that describe inter-dependence between spatial locations (and can therefore be difficult to model and compute). However, similar models can be developed more conveniently in Fourier transformed space as a large set of independent processes. The originally complex high-dimensional estimation
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  Statistical methods to evaluate the effectiveness of cancer treatments in clinical trials

  Denis Rustand, Postdoctoral Research Fellow (former), Statistics

  Oct 14, 12:00 - 13:00

  KAUST

  Assessing the effectiveness of cancer treatments in clinical trials raises multiple methodological challenges that need to be properly addressed in order to produce a reliable estimate of treatment effects.
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  Parameter Estimation for the McKean-Vlasov Stochastic Differential Equation

  Nikolas Kantas, Associate Professor, Department of Mathematics, Imperial College London

  Oct 13, 15:30 - 16:30

  KAUST

  parameter estimation stochastic differential equations stochastic

  We consider the problem of parameter estimation for a McKean stochastic differential equation, and the associated system of weakly interacting particles. The problem is motivated by many applications in areas such as neuroscience, social sciences (opinion dynamics, cooperative behaviours), financial mathematics, statistical physics. We will first survey some model properties related to propagation of chaos and ergodicity and then move on to discuss the problem of parameter estimation both in offline and on-line settings. In the on-line case, we propose an online estimator, which evolves according to a continuous-time stochastic gradient descent algorithm on the asymptotic log-likelihood of the interacting particle system. The talk will present our convergence results and then show some numerical results for two examples, a linear mean field model and a stochastic opinion dynamics model. This is joint work with Louis Sharrock, Panos Parpas and Greg Pavliotis. Preprint: https://arxiv.org/abs/2106.13751

Oct 3 - Oct 9, 2021

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  Quantifying Uncertainty with a Derivative Tracking SDE Model and Application to Wind Power Forecast Data

  Raul Tempone, Professor, Applied Mathematics and Computational Science

  Oct 7, 12:00 - 13:00

  KAUST

  We develop a data-driven methodology based on parametric Itô's Stochastic Differential Equations (SDEs) to capture forecast errors' asymmetric dynamics, including the forecast's uncertainty at time zero.

Sep 26 - Oct 2, 2021

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  Discriminant analysis: Fundamental limits and Optimal designs

  Abla Kammoun, Senior Research Scientist, Electrical and Computer Engineering

  Sep 30, 12:00 - 13:00

  KAUST

  Despite the recent advances in big data processing, enabled by the emergence of large-scale machine learning techniques, several statistical questions regarding the behavior in the regime of high dimensions of well-established and fundamental methods have remained unresolved.
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  Randomized multilevel Monte Carlo for inference

  Kody J.H. Law, Professor, Applied Mathematics in the Department of Mathematics, University of Manchester and Manchester Institut

  Sep 29, 13:00 - 14:00

  KAUST

  Often in the context of data centric science and engineering applications, one endeavours to learn complex systems in order to make more informed predictions and high stakes decisions under uncertainty. Some key challenges which must be met in this context are robustness, generalizability, and interpretability.

Sep 12 - Sep 18, 2021

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  Developing Aramco’s Next Generation Operational Systems for the Red Sea and the Arabian Gulf

  Ibrahim Hoteit, Professor, Earth Science and Engineering

  Sep 16, 12:00 - 13:00

  KAUST

  The talk will present our efforts to develop the next generation operational systems for the Red Sea and the Arabian Gulf, as part of Aramco’s resolution toward the Fourth Industrial Revolution. These integrated systems, we refer to as iReds and iGulf, have been built around state-of-the-art ocean-atmosphere-wave general circulation models that have been specifically developed for the region and nested within the global weather systems.

Sep 5 - Sep 11, 2021

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  Empirical Risk Minimization in the Non-interactive LDP Model

  Di Wang, Assistant Professor, Computer Science

  Sep 9, 12:00 - 13:00

  KAUST

  As a fundamental problem in both machine learning and privacy, Empirical Risk Minimization in the Differential Privacy Model (DP-ERM) received much attentions. However, most of the previous studies are either in the central DP model or interactive LDP model. In this talk, I will discuss some recent developments of DP-ERM in the non-interactive LDP model.

Aug 29 - Sep 4, 2021

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  High-resolution Modeling and Estimation of Extreme Red Sea Surface Temperature Hotspots

  Raphaël Huser, Associate Professor, Statistics

  Sep 2, 12:00 - 13:00

  KAUST

  Modeling, estimation and prediction of spatial extremes is key for risk assessment in a wide range of geo-environmental, geo-physical, and climate science applications. In this work, we propose a flexible approach for modeling and estimating extreme sea surface temperature (SST) hotspots, i.e., high threshold exceedance regions, for the whole Red Sea, a vital region of high biodiversity.
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  Sequential Monte Carlo Algorithms for Agent-Based Models of Disease Transmission

  Jeremy Heng, Applied Mathematics and Computational Sciences (AMCS), ESSEC Business School, Singapore

  Sep 1, 13:00 - 14:00

  KAUST

  We consider statistical inference for a class of agent-based SIS and SIR models. In these models, agents infect one another according to random contacts made over a social network, with an infection rate that depends on individual attributes. Infected agents might recover according to another random mechanism that also depends on individual attributes, and observations might involve occasional noisy measurements of the number of infected agents. Likelihood-based inference for such models presents various computational challenges. In this talk, I will present various sequential Monte Carlo algorithms to address these challenges.

Aug 15 - Aug 21, 2021

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  Machine Learning in Healthcare: When Low Sample Size is not a Limitation

  Dr. Ricardo Henao, Biostatistics and Bioinformatics, Duke University

  Aug 17, 14:30 - 15:30

  KAUST

  In this talk, I will describe three use cases that highlight present challenges and opportunities for the development of machine learning methodology for applications in healthcare. First, I will describe the development of simple word embedding approaches for bag of-documents classification and its applications to diagnosis of peripheral artery disease from clinical narratives. Second, I will present an approach for volumetric image classification that leverages attention mechanisms, contrastive learning and feature-encoding sharing for geographic atrophy prognosis from optical coherence tomography images. Third, I will discuss machine learning approaches for multi-modal and multi-dataset integration for biomarker discovery from molecular (omics) data. To conclude, I will summarize the contributions and insights in each of these different directions in which relatively low sample sizes are the common denominator.

Jun 6 - Jun 12, 2021

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  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

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  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.
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  Flexible Covariance Models for Spatio-Temporal and Multivariate Spatial Random Fields

  Ghulam A. Qadir, Ph.D. Student, Statistics

  May 31, 16:00 - 18:00

  KAUST

  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.

May 2 - May 8, 2021

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  Scalable Solvers: Universals and Innovations

  David Keyes, Professor, Applied Mathematics and Computational Science

  May 6, 12:00 - 13:00

  KAUST

  Abstract As simulation and analytics enter the exascale era, numerical algorithms must span a widening gap between ambitious applications and austere architectures. We present fifteen universals for researchers in scalable solvers and some innovations that allow approaching lin-log complexity in storage and operation count in many important algorithmic kernels. Applications are becoming ambitious in many senses: large physical space, phase space or parameter dimensions; resolution of many scales of space and/or time; high fidelity physical modeling; linking together of multiple complex models

Apr 25 - May 1, 2021

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  The Role of Volcanic Activity in Climate and Global Change

  Georgiy L. Stenchikov, Professor, Earth Science and Engineering

  Apr 29, 12:00 - 13:00

  KAUST

  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.
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  Design and Analysis of Prevalence Surveys for Neglected Tropical Diseases

  Prof. Peter Diggle, Statistics in the faculty of Health and Medicine, Lancaster University

  Apr 27, 15:00 - 16:30

  KAUST

  Design and analysis of surveys

  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.

Apr 18 - Apr 24, 2021

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  Distributed second order methods with fast rates and compressed communication

  Peter Richtarik, Professor, Computer Science

  Apr 22, 12:00 - 13:00

  KAUST

  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.

Apr 4 - Apr 10, 2021

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  A Rapid, Accurate and Machine-agnostic Segmentation and Quantification Method for CT-based COVID-19 Diagnosis

  Xin Gao, Program Chair, Computer Science

  Apr 8, 12:00 - 13:00

  KAUST

  COVID-19

  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.

Mar 28 - Apr 3, 2021

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  Waves Functional Materials - 2021-04-01

  Ying Wu, Program Chair, Applied Mathematics and Computational Science

  Apr 1, 12:00 - 13:00

  KAUST

  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.

Mar 21 - Mar 27, 2021

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  The Convex Gaussian Min-Max Theorem: A Powerful Tool for the Analysis of Regularized Convex Optimization Problems

  Tareq Al-Naffouri, Professor, Electrical and Computer Engineering

  Mar 25, 12:00 - 13:00

  KAUST

  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.

Mar 14 - Mar 20, 2021

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  On Discovering Nature’s Equations from Observations

  Jesper Tegner, Professor, BESE Division, KAUST

  Mar 18, 12:00 - 13:00

  KAUST

  In essence, science is about discovering regularities in Nature. It turns out that such regularities (laws) are written in the language of mathematics. In many cases, such laws are formulated and refined from fundamental “first principles.” Yet, in phenomenological areas such as biology, we have an abundance of data but lack “first principles.” Machine learning and deep learning, in particular, are remarkably successful in classification and prediction tasks. However, such systems, when trained on data, do not, as a rule, provide compact mathematical laws or fundamental first principles. Here we ask how we can identify interpretable compact mathematical laws from complex data-sets when we don’t have access to first principles. I will give an overview of this problem and provide some vignettes of our ongoing work in attacking this problem.

Mar 7 - Mar 13, 2021

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  Programming curvature into flat sheets

  Mar 11, 12:00 - 13:00

  KAUST

  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.
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  Analysis of the compressible two-phase flow

  Dr. Ewelina Zatorska, Senior Lecturer in the Applied and Numerical Analysis, Imperial College London

  Mar 9, 15:00 - 16:00

  KAUST

  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.

Feb 21 - Feb 27, 2021

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  On Adversarial Network Attacks, Robustness, and Certification

  Bernard Ghanem, Professor, Electrical and Computer Engineering

  Feb 25, 12:00 - 13:00

  KAUST

  Abstract The outstanding performance of deep neural networks (DNNs), for visual recognition tasks in particular, has been demonstrated on many large-scale benchmarks. This performance has immensely strengthened the line of research that aims to understand and analyze the driving reasons behind the effectiveness of these networks. One important aspect of this analysis has gained much attention, namely the sensitivity of a DNN to perturbations. This has spawned a thrust in the research community, which focuses on developing adversarial attacks that fool a DNN, training strategies that make DNNs

Feb 14 - Feb 20, 2021

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  Forward-prediction and Backward-design by Machine Learning

  Prof. Xiangliang Zhang

  Feb 18, 12:00 - 13:00

  KAUST

  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.
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  High-dimensional inference for max-stable processes

  Dr. Boris Beranger, Lecturer in Statistics, University of New South Wales, Sydney

  Feb 16, 11:00 - 12:00

  KAUST

  extreme environmental events Max-stable processes

  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.

Feb 7 - Feb 13, 2021

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  Proper scoring rules and model selection for stochastic processes

  David Bolin, Professor, Statistics

  Feb 11, 12:00 - 13:00

  KAUST

  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.
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  Statistical models for studying biodiversity: challenges and contributions

  Antik Chakraborty, Postdoc, Department of Statistical Science, Duke University

  Feb 7, 16:00 - 17:00

  KAUST

  biodiversity

  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.