Events

Jun 5 - Jun 11, 2022

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  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

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  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

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  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.
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  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.
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  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.
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  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:
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  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.
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  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.
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  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.
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  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

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  Gaussian Graphical Regression with High-Dimensional Responses and Covariates

  Yi Li, Professor, Biostatistics, University of Michigan

  Apr 21, 16:30 - 17:30

  B2 B3 A0215

  Though Gaussian graphical models have been widely used in many scientific fields, relatively limited progress has been made to link graph structures to external covariates. We propose a Gaussian graphical regression model, which regresses both the mean and the precision matrix of a Gaussian graphical model on covariates. In the context of co-expression quantitative trait locus (QTL) studies, our method can determine how genetic variants and clinical conditions modulate the subject-level network structures, and recover both the population-level and subject-level gene networks.
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  Enhancing mixing in binary fluids

  Prof. Peter J. Schmid, Professor, Mechanical Engineering, KAUST

  Apr 21, 12:00 - 13:00

  B9 L2 H2

  Mixing is a key process in a wide variety of technological applications. Pharmaceuticals, consumer products, and oil & gas derivatives, among many other products, rely on an energy-efficient, robust and effective mixing process. We will formulate the mixing process as a PDE-constrained optimization problem using special norms that concentrate on the mixedness of a passive scalar transported by nonlinear governing equations.
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  Regularized Regression for Survival Data: Methods and Applications

  Yi Li, Professor, Biostatistics, University of Michigan

  Apr 19, 16:00 - 18:00

  B1 L4 R4102

  In the era of biomedical big data, survival outcome data with high-throughput predictors are routinely collected. These high dimensional data defy classical survival regression models, which are either infeasible to fit or likely to incur low predictability because of overfitting. This short course will introduce the basic concepts of survival analysis and various cutting-edge methods that handle survival outcome data with high dimensional predictors. I will cover statistical principles and concepts behind the methods, and will also discuss their applications to the real medical examples.

Apr 10 - Apr 16, 2022

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  Biomedical research: a world of statistical challenges

  Prof. David Gomez-Cabrero, Biological, Environmental Science and Eng, KAUST

  Apr 14, 12:00 - 13:00

  B9 L2 H2

  A personal presentation describing current “data analysis / statistical” challenges in state-of-the art biomedical projects. First, I will provide an overview of the transition between a PhD in Mathematics to a postdoc in Computational Biology: How did it happen? What were the challenges? Secondly, I will briefly present several current case-studies where statistics and machine learning are core to understand novel biological data related to multi-omic data analysis, spatial profiling, gene therapy and more.
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  Causal aggregation: estimation and inference of causal effects by aggregating information across data sets

  Prof. Dominik Rothenhaeusler, Statistics, Stanford University

  Apr 12, 17:00 - 18:00

  KAUST

  Randomized experiments are the gold standard for causal inference. In experiments, usually, one variable is manipulated and its effect is measured on an outcome. However, practitioners may also be interested in the effect of simultaneous interventions on multiple covariates on a fixed target variable.

Apr 3 - Apr 9, 2022

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  Phasewise-Conservative and Bound-Preserving Algorithms for Two-Phase Flow in Porous Media

  Shuyu Sun, Professor, Earth Science and Engineering

  Apr 7, 12:00 - 13:00

  B9 L2 H2

  Two or multiple phases in fluid mixture commonly occur in petroleum industry, where oil, gas and water are often produced and transported together. Petroleum reservoir engineers spent great efforts in drainage problems arising from the development and production of oil and gas reservoirs so as to obtain a high economic recovery, by developing, conducting, and interpolating the simulation of subsurface flows of reservoir fluids, including water, hydrocarbon, CO2, H2S for example in porous geological formation.
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  Bayesian Modeling of Sub-Asymptotic Spatial Extremes

  Rishikesh Yadav, Ph.D. Student, Statistics

  Apr 5, 15:00 - 17:00

  B5 L5 R5220

  spatial statistics extreme-value theory spatio-temporal statistics bayesian inference

  In this thesis, we develop new flexible sub-asymptotic extreme value models for modeling spatial and spatio-temporal extremes that are combined with carefully designed gradient-based Markov chain Monte Carlo (MCMC) sampling schemes and that can be exploited to address important scientific questions related to risk assessment in a wide range of environmental applications. The methodological developments are centered around two distinct themes, namely (i) sub-asymptotic Bayesian models for extremes; and (ii) flexible marked point process models with sub-asymptotic marks. In the first part, we develop several types of new flexible models for light-tailed and heavy-tailed data, which extend a hierarchical representation of the classical generalized Pareto (GP) limit for threshold exceedances. Spatial dependence is modeled through latent processes. We study the theoretical properties of our new methodology and demonstrate it by simulation and applications to precipitation extremes in both Germany and Spain.
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  Modeling and Simulation of Spatial Extremes Based on Max-Infinitely Divisible and Related Processes

  Peng Zhong, Ph.D. Student, Statistics

  Apr 4, 17:00 - 19:00

  B3 L5 R5209

  The statistical modeling of extreme natural hazards is becoming increasingly important due to climate change, whose effects have been increasingly visible throughout the last decades. It is thus crucial to understand the dependence structure of rare, high-impact events over space and time for realistic risk assessment. For spatial extremes, max-stable processes have played a central role in modeling block maxima. However, the spatial tail dependence strength is persistent across quantile levels in those models, which is often not realistic in practice. This lack of flexibility implies that max-stable processes cannot capture weakening dependence at increasingly extreme levels, resulting in a drastic overestimation of joint tail risk.

Mar 27 - Apr 2, 2022

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  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.

Mar 20 - Mar 26, 2022

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  Regression models for count and proportion data

  Alexandre de Bustamante Simas , Research Scientist, Statistics

  Mar 24, 12:00 - 13:00

  B9 L2 H2

  Abstract In this talk we will present two classes of regression models. The first class is focused on overdispersed count models and is given by assuming that the response variable is obtained by mixing the Poisson distribution with an absolutely continuous distribution in the exponential family. The second class is focused on proportion data and assumes that the response variable follows a distribution given by the fraction $X/(X+Y)$, where $X$ and $Y$ are independent absolutely continuous random variables from the exponential family. Usually, the marginal likelihood of the response variables

Mar 13 - Mar 19, 2022

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  The Maxwell-Stefan system for multi-component diffusion

  Athanasios Tzavaras, Professor, Applied Mathematics and Computational Sciences

  Mar 17, 12:00 - 13:00

  B9 L2 H2

  The Maxwell-Stefan system is a system of equations commonly used to describe diffusion processes of multi-component systems. In this talk (i) I will describe modeling of multi-component systems, which leads to extensions of the Euler compressible dynamics system with mass and thermal diffusion. (ii) Will describe how the Maxwell-Stefan system emerges in the high-friction limit of multi-component Euler flows. (iii) Discuss some mathematical questions that this model raises and on the construction of numerical schemes for the Maxwell-Stefan system associated with the minimization of frictional dissipation.

Mar 6 - Mar 12, 2022

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  Machine learning assisted forward and inverse design modeling

  Waqas Waseem Ahmed, Postdoctoral Research Fellow, Applied Mathematics and Computational Sciences

  Mar 10, 12:00 - 13:00

  KAUST

  In recent years, machine learning has proven to be efficient in solving various physical problems through data-driven approaches. For example, in wave physics, models based on analytical and numerical schemes employ intensive trial-and-error tuning of material (and geometrical) parameters for 'on demand' wave properties, which require deep understanding of the physics and are computationally expensive. As a result, it is desired to develop intelligent models that learn the bidirectional mapping between different physical quantities and automate technological device design. In this presentation, I will discuss novel generative models for forward and inverse predictions that outperform human performance. In particular, I will show how machine learning can be used to design broadband acoustic cloaks, unidirectional non-Hermitian structures, and for solving the inverse scattering problem of shape recognition.
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  Coupled Sampling Methods for Filtering

  Fangyuan Yu, Ph.D. Student, Statistics

  Mar 7, 15:00 - 17:00

  KAUST

  Monte carlo methods computational statistics Markov models

  This thesis focuses on the use of multilevel Monte Carlo methods to achieve optimal error versus cost performance for statistical computations in hidden Markov models as well as for unbiased estimation under four cases: nonlinear filtering, unbiased filtering, unbiased estimation of hessian, continuous linear Gaussian filtering.

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 Sciences

  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, Applied Mathematics and Computational Sciences

  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 Sciences

  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 Sciences

  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, Program Chair, Applied Mathematics and Computational Sciences

  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, 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 Sciences

  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.