Events

Mar 28 - Apr 4, 2021

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  Waves Functional Materials - 2021-04-01

  Ying Wu, Professor, Applied Mathematics and Computational Sciences

  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 28, 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 21, 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 14, 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 28, 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 21, 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 14, 2021

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  Proper scoring rules and model selection for stochastic processes

  David Bolin, Associate 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.

Jan 31 - Feb 14, 2021

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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.

Jan 31 - Feb 7, 2021

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  Effective Literature Search

  Nevena Tomic, Library Subject Specialist, PSE, KAUST

  Feb 4, 12:00 - 13:00

  KAUST

  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.

Jan 24 - Jan 31, 2021

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  A personal and historical view of computational mathematics

  Tony Chan, President, King Abdullah University of Science and Technology

  Jan 28, 12:00 - 13:00

  KAUST

  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.

Dec 6 - Dec 13, 2020

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  Geospatial Data Science for Public Health Surveillance - Graduate Seminar

  Dec 10, 12:00 - 13:00

  KAUST

  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.

Nov 29 - Dec 6, 2020

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  Modelling and Simulation of Biological Systems - 2020-12-03

  Gabriel Wittum, Professor, Applied Mathematics and Computational Sciences

  Dec 3, 12:00 - 13:00

  KAUST

  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.
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  Transparent and Robust Causal Inference in Data Science

  Carlos Cinelli, Ph.D. candidate, Department of Statistics, UCLA

  Nov 30, 16:30 - 17:30

  KAUST

  statistical inference data science

  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.

Nov 22 - Nov 29, 2020

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  BICNet: A Bayesian Approach for Estimating Task Effects on Intrinsic Connectivity Networks in fMRI Data

  Meini Tang, M.S. Student, Statistics

  Nov 24, 09:00 - 10:00

  KAUST

  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.

Nov 15 - Nov 22, 2020

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  On Unbiased Estimation of the Score Function for a Class of Partially Observed Diffusions

  Nov 19, 12:00 - 13:00

  KAUST

  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)
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  Start your Research Journey at KAUST - Master and Ph.D. degrees in Applied Mathematics, Computer Science, Electrical Engineering and Statistics

  Aigars Ekers, Assistant Dean, Computer, Electrical and Mathematical Sciences and Engineering

  Nov 18, 16:00 - 17:00

  KAUST

  Did you know that courses are taught in English at KAUST? Did you know that the faculty and students are from more than 100 countries? Did you know that you will be fully funded when studying at KAUST? What you will learn from this webinar: Life at KAUST: What are KAUST and CEMSE? Degrees and Academic Programs Research Areas and Lab Facilities Eligibility and Admission Requirements KAUST Fellowship: Benefits of studying at KAUST How to Apply?

Nov 8 - Nov 15, 2020

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  Optimization applied to energy systems, autonomous vehicles, and matching

  Ricardo Manuel Pinto de Lima, Research Scientist, Applied Mathematics and Computational Sciences

  Nov 12, 12:00 - 13:00

  KAUST

  In this talk, we start by introducing optimization and interesting optimization applications. We review some optimization formulations and focus on applications studied in our research, such as energy systems, and trajectory planning of autonomous underwater vehicles. After the introduction, we address the self-scheduling and market involvement of a virtual power plant using adaptive robust optimization under uncertainty in the wind speed and electricity prices.
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  Quantile Function Modeling and Analysis for Multivariate Functional Data

  Gaurav Agarwal, Ph.D., Statistics

  Nov 9, 16:00 - 18:00

  KAUST

  This thesis presents a set of quantile analysis methods for multivariate data and multivariate functional data, with an emphasis on environmental applications, and consists of four significant contributions.

Nov 1 - Nov 8, 2020

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  Exploring Non-Linear Interactions in Multivariate Time Series

  Hernando Ombao, Professor, Statistics

  Nov 5, 12:00 - 13:00

  KAUST

  Linear Multivariate extremes Bayesian computational statistics

  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.
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  Spatio-Temporal Prediction and Stochastic Simulation for Large-Scale Nonstationary Processes

  Yuxiao Li, Ph.D., Statistics

  Nov 2, 16:00 - 18:00

  KAUST

  Environmental statistics plays an important role in many related applications, such as weather-related risk assessment for urban design and crop growth. However, modeling the spatio-temporal dynamics of environmental data is challenging due to their inherent high variability and nonstationarity. This dissertation focuses on the modeling, simulation, and prediction of spatio-temporal processes using statistical techniques and machine learning algorithms, especially for nonstationary processes.

Oct 25 - Nov 1, 2020

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  On the approximation of the spectrum of differential operators

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

  Oct 29, 12:00 - 13:00

  KAUST

  Eigenvalue problems associated with partial differential equations are key ingredients for the modeling and simulation of a variety of real world applications, ranging from fluid-dynamics, structural mechanics, acoustics, to electromagnetism and medical problems. We review some properties related to the approximation of eigenvalue problems. Starting from matrix algebraic problems, we present a series of examples and counterexamples showing that even extremely simple situations can produce unexpected results.
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  Spatio-Temporal Statistical Modeling with Application to Wind Energy Assessment in Saudi Arabia

  Wanfang Chen, Ph.D., Statistics

  Oct 26, 15:00 - 17:00

  KAUST

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

Oct 18 - Oct 25, 2020

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  Efficiency Assessment of Approximated Spatial Predictions for Large Datasets

  Yiping Hong, Postdoctoral Research Fellow, Statistics

  Oct 22, 12:00 - 13:00

  KAUST

  mathematics applied statistics Social Network Analysis

  Our suggested criteria are more useful for the determination of tuning parameters for sophisticated approximation methods of spatial model fitting. To illustrate this, we investigate the trade-off between the execution time, estimation accuracy, and prediction efficiency for the TLR method with intensive simulation studies and suggest proper settings of the TLR tuning parameters.

Oct 11 - Oct 18, 2020

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  Epidemiological modeling and optimal control

  David Ketcheson, Professor, Applied Mathematics and Computational Sciences

  Oct 15, 12:00 - 13:00

  KAUST

  optimal control optimal control theory

  Compartmental epidemiological models are one of the simplest models for the spread of a disease. They are based on statistical models of interactions in large populations and can be effective in the appropriate circumstances. Their application historically and in the present pandemic has sometimes been successful and sometimes spectacularly wrong. In this talk I will review some of these models and their application. I will also discuss the behavior of the corresponding dynamical systems, and discuss how the theory of optimal control can be applied to them. I will describe some of the challenges in using such a theory to make decisions about public policy.

Oct 4 - Oct 11, 2020

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  ExaGeoStat: Moving Towards Large-Scale Geostatistical Modeling on Manycore Systems

  Sameh Abdulah, Research Scientist, Hierarchical Computations on Manycore Architectures

  Oct 8, 12:00 - 13:00

  KAUST

  Adaptive modeling

  We present Exascale GeoStatistics (ExaGeoStat) software, a high-performance library implemented on a wide variety of contemporary hybrid distributed-shared supercomputers whose primary target is climate and environmental prediction applications.

Sep 27 - Oct 4, 2020

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  Consensus formation models with delay

  Jan Haskovec, AMCS, KAUST

  Oct 1, 12:00 - 13:00

  KAUST

  Models Formation

  Individual-based models of collective behavior represent a very active research field with applications in physics (spontaneous magnetization), biology (flocking and swarming) and social sciences (opinion formation). They are also a hot topic engineering (swarm robotics). A particularly interesting aspect of the dynamics of multi-agent systems is the emergence of global self-organized patterns, while individuals typically interact only on short scales. In this talk I shall discuss the impact of delay on asymptotic consensus formation in Hegselmann-Krause-type models, where agents adapt their „opinions“ (in broad sense) to the ones of their close neighbors. We shall understand the two principial types/sources of delay - information propagation and processing - and explain their qualitatively different impacts on the consensus dynamics. We then discuss various mathematical methods that provide asymptotic consensus results in the respective settings: Lyapunov functional-type approach, direct estimates, convexity arguments and forward-backward estimates.

Sep 13 - Sep 20, 2020

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  Estimating High-Resolution Red Sea Surface Temperature Hotspots, Using a Low-Rank Semiparametric Spatial Model

  Arnab Hazra, Postdoctoral Research Fellow, Statistics

  Sep 17, 12:00 - 13:00

  KAUST

  High Resolution X-ray Diffraction spatial modulation

  In this work, we estimate extreme sea surface temperature (SST) hotspots, i.e., high threshold exceedance regions, for the Red Sea, a vital region of high biodiversity. We analyze high-resolution satellite-derived SST data comprising daily measurements at 16703 grid cells across the Red Sea over the period 1985–2015. We propose a semiparametric Bayesian spatial mixed-effects linear model with a flexible mean structure to capture spatially-varying trend and seasonality, while the residual spatial variability is modeled through a Dirichlet process mixture (DPM) of low-rank spatial Student-t processes (LTPs). By specifying cluster-specific parameters for each LTP mixture component, the bulk of the SST residuals influence tail inference and hotspot estimation only moderately. Our proposed model has a nonstationary mean, covariance and tail dependence, and posterior inference can be drawn efficiently through Gibbs sampling. In our application, we show that the proposed method outperforms some natural parametric and semiparametric alternatives.

Sep 6 - Sep 13, 2020

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  A cheap rotated characteristic decomposition technique for high-order reconstructions in multi-dimensions with application to instabilities in direct initiation of gaseous detonations in free space

  Matteo Parsani, Associate Professor, Applied Mathematics and Computational Sciences

  Sep 10, 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. For multi-dimensional finite volume schemes, we need to perform the characteristic decomposition several times in different normal directions of the target cell, which is very time-consuming. We propose a rotated characteristic decomposition technique that requires only one-time decomposition for multi-dimensional reconstructions. This technique not only reduces the computational cost remarkably, but also controls spurious oscillations effectively. We take a third-order weighted essentially non-oscillatory finite volume scheme for solving the Euler equations as an example to demonstrate the efficiency of the proposed technique. We apply the new methodology to the simulation of instabilities in direct initiation of gaseous detonations in free space.

Aug 30 - Sep 6, 2020

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  The two faces of correlation

  Haavard Rue, Program Chair, Statistics

  Sep 3, 12:00 - 13:00

  KAUST

  Statistical Modeling computing

  Discussing the concept of correlation and how to interpret it alone (marginally) or within a more complex environment (conditionally). This rather simple observation is the key observation behind a lot of exciting developments and connections in statistics that can be leveraged for improved computations and better motivated statistical models.

Apr 26 - May 3, 2020

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  On Optimal Regularization in Estimation, Detection, and Classification

  Tareq Al-Naffouri, Professor, Electrical and Computer Engineering

  Apr 30, 12:00 - 13:00

  KAUST

  linear systems computational Complexity Convex Gaussian Min-max theorem

  In many problems in statistical signal processing, regularization is employed to deal with uncertainty, ill-posedness, and insufficiency of training data. It is possible to tune these regularizers optimally asymptotically, i.e. when the dimension of the problem becomes very large, by using tools from random matrix theory and Gauss Process Theory. In this talk, we demonstrate the optimal turning of regularization for three problems : i) Regularized least squares for solving ill-posed and/or uncertain linear systems, 2) Regularized least squares for signal detection in multiple antenna communication systems and 3) Regularized linear and quadratic discriminant binary classifiers.

Apr 19 - Apr 26, 2020

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  Efficient and Physics-Preserving Discretization Schemes for Multiphase Flow Problems

  Shuyu Sun, Professor, Earth Science and Engineering

  Apr 23, 12:00 - 13:00

  KAUST

  No summary is available.

Apr 12 - Apr 19, 2020

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  Quantitative understanding of transcription factor regulation at network- and molecular-levels through optimization and deep learning

  Xin Gao, Program Chair, Computer Science

  Apr 16, 12:00 - 13:00

  KAUST

  transcription factor optimization Deep learning

  Transcription factors are an important family of proteins that control the transcription rate from DNAs to messenger RNAs through the binding to specific DNA sequences. Transcription factor regulation is thus fundamental to understanding not only the system-level behaviors of gene regulatory networks, but also the molecular mechanisms underpinning endogenous gene regulation. In this talk, I will introduce our efforts on developing novel optimization and deep learning methods to quantitatively understanding transcription factor regulation at network- and molecular-levels. Specifically, I will talk about how we estimate the kinetic parameters from sparse time-series readout of gene circuit models, and how we model the relationship between the transcription factor binding sites and their binding affinities.

Apr 5 - Apr 12, 2020

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  Geometry and Computational Design

  Apr 9, 12:00 - 13:00

  KAUST

  Geometry computational design

  An important stream of research in computational design aims at digital tools which support users in realizing their design intent in a simple and intuitive way, while simultaneously taking care of key aspects of function and fabrication. Such tools are expected to shorten the product development cycle through a reduction of costly feedback loops between design, engineering and fabrication. The strong coupling between shape generation, function and fabrication is a rich source for the development of new geometric concepts, with an impact to the original applications as well as to geometric theory. This will be illustrated at hand of applications in architecture and fabrication with a mathematical focus on discrete differential geometry and geometric optimization problems.
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  Computation of High-Dimensional Multivariate Normal and Student-t Probabilities Based on Matrix Compression Schemes

  Jian Cao, Ph.D., Statistics

  Apr 6, 16:00 - 18:00

  KAUST

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

Mar 29 - Apr 5, 2020

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  Classification of Functional Data by Detecting the Discrepancy of Second Moment Structure of Scaled functions

  Shuhao Jiao, Postdoctoral Research Fellow, Statistics

  Apr 2, 12:00 - 13:00

  KAUST

  Functional data

  This talk presents a new classification method for functional data. We consider the case where different groups of functions have similar means so that it is difficult to classify them based on only the mean function. To overcome this limitation, we propose the second moment based functional classifier (SMFC). Here, we demonstrate that the new method is sensitive to divergence in the second moment structure and thus produces lower rate of misclassification compared to other competitor methods. Our method uses the Hilbert-Schmidt norm to measure the divergence of second moment structure. One important innovation of our classification procedure lies in the dimension reduction step. The method data-adaptively discovers the basis functions that best capture the discrepancy between the second moment structures of the groups, rather than uses the functional principal component of each individual group, and good performance can be achieved as unnecessary variability is removed so that the classification accuracy is improved. Consistency properties of the classification procedure and the relevant estimators are established. Simulation study and real data analysis on phoneme and rat brain activity trajectories empirically validate the superiority of the proposed method.

Mar 8 - Mar 15, 2020

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  Functional data depth and its application in the visualization of spatio-temporal covariance structures

  Mar 12, 12:00 - 13:00

  KAUST

  functional data analysis spatio-temporal statistics

  Functional data analysis is a very active research area due to the overwhelming existence of functional data. In the first part of this talk, I will introduce how functional data depth is used to carry out exploratory data analysis and explain recently-developed depth techniques. In the second part, I will discuss spatio-temporal statistical modeling. It is challenging to build realistic space-time models and assess the validity of the model, especially when datasets are large. I will present a set of visualization tools we developed using functional data analysis techniques for visualizing covariance structures of univariate and multivariate spatio-temporal processes. I will illustrate the performance of the proposed methods in the exploratory data analysis of spatio-temporal data.

Mar 1 - Mar 8, 2020

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  Simulation of Neuronal Signal Processing - 2020-03-05

  Gabriel Wittum, Professor, Applied Mathematics and Computational Sciences

  Mar 5, 12:00 - 13:00

  B9 L2 R2322

  Neuronal ensembles Stochastiic Variability neuroscience

  In the lecture we present a three dimensional mdoel for the simulation of signal processing in neurons. To handle problems of this complexity, new mathematical methods and software tools are required. In recent years, new approaches such as parallel adaptive multigrid methods and corresponding software tools have been developed allowing to treat problems of huge complexity. Part of this approach is a method to reconstruct the geometric structure of neurons from data measured by 2-photon microscopy. Being able to reconstruct neural geometries and network connectivities from measured data is the basis of understanding coding of motoric perceptions and long term plasticity which is one of the main topics of neuroscience. Other issues are compartment models and upscaling.

Feb 16 - Feb 23, 2020

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  Statistical models for long-range dependent climate data

  Sigrunn Sorbye, Associate Professor, UiT The Arctic University of Norway

  Feb 20, 12:00 - 13:00

  B9 L2 R2322

  statistical models climate data

  In this talk I will discuss statistical models which incorporate temperature response to the radiative forcing components. The models can be used to estimate important climate sensitivity measures and give temperature forecasts. Bayesian inference is obtained using the methodology of integrated nested Laplace approximation and Monte Carlo simulations. The resulting approach will be demonstrated in analyzing instrumental data and Earth system model ensembles.

Feb 2 - Feb 9, 2020

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  Mathematical modeling of the stochastic climate system

  Georgiy L. Stenchikov, Professor, Earth Science and Engineering

  Feb 6, 12:00 - 13:00

  B9 L2 H1

  weather climate projections

  ​Author of more than 290 journal and conference publications, Professor Stenchikov's research interests are in multi-scale modeling of environmental processes and numerical methods; global climate change, climate downscaling, atmospheric convection; assessment of anthropogenic impacts and geoengineering; air-sea interaction, evaluating environmental consequences of catastrophic events like volcanic eruptions, nuclear explosions, forest and urban fires; and air pollution, transport of aerosols, chemically and optically active atmospheric tracers, their radiative forcing and effect on climate.
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  Geospatial Data Science for Public Health Surveillance - Seminar

  Paula Moraga, Lecturer, Department of Mathematical Sciences, University of Bath, UK

  Feb 5, 12:00 - 13:00

  B9 L2 H2

  Geospatial Data Public Health

  In this talk, I will give an overview of my research which focuses on the development of innovative statistical methods and interactive visualization applications for geospatial data analysis and health surveillance. I will illustrate some of my projects in the following areas: 1. Development of new statistical methodology; 2. Development of open-source statistical software such as the R packages; 3. Health surveillance projects. Finally, I will describe my future research on innovation in data acquisition and visualization, precision disease mapping, and digital health surveillance, and how it can inform policymaking and improve population health globally.

Nov 24 - Dec 1, 2019

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  Statistical Analysis of Multi-day Solar Irradiance using a Threshold Time Series model

  Carolina D. Euan Campos, Postdoctoral Research Fellow, Statistics

  Nov 28, 12:00 - 13:00

  B9 H2 R2325

  solar energy solar irradiance analysis nonlinearity TAR model statistical analysis forecasting

  Abstract The analysis of solar irradiance has important applications in predicting solar energy production from solar power plants. Although the sun provides every day more energy than we need, the variability caused by environmental conditions affects electricity production. Most of the existing statistical models to forecast solar irradiance are linear and highly depend on normality assumptions. However, solar irradiance shows strong non-linearity and is only measured during the day time. Thus, we propose a new multi-day threshold autoregressive (TAR) model to quantify the variability of the
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  Asymptotically Exact Variational Bayes for High-Dimensional Probit Models

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

  Nov 27, 15:30 - 16:30

  B1 L4 R4102

  Bayesian approach to inference decision sciences

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

  Prof. Ben Zhao, Computer Science, University of Chicago, USA

  Nov 25, 12:00 - 13:00

  B9 L2 H1 R2322

  In this talk, I will describe two recent results on detecting and understanding backdoor attacks on deep learning systems. I will first present Neural Cleanse (IEEE S&P 2019), the first robust tool to detect a wide range of backdoors in deep learning models. We use the idea of perturbation distances between classification labels to detect when a backdoor trigger has created shortcuts to misclassification to a particular label. Second, I will also summarize our new work on Latent Backdoors (CCS 2019), a stronger type of backdoor attack that is more difficult to detect and survives retraining in commonly used transfer learning systems. Latent backdoors are robust and stealthy, even against the latest detection tools (including neural cleanse).

Nov 17 - Nov 24, 2019

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  Estimation Problems for Partial and Fractional Differential Equations - Autumn 2019

  Nov 21, 12:00 - 13:00

  B9 L2 H1 R2322

  Fractional differential equations Partial Differential Equations Estimation problems Simulations algorithms real applications

  I will present an overview of our activities around estimation problems for partial and fractional differential equations. I will present the methods and the algorithms we develop for the state, source and parameters estimation and illustrate the results with some simulations and real applications.
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  2019 Statistics and Data Science Workshop

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

  B2 B3 A0215

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

Nov 10 - Nov 17, 2019

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  Statistical models based on stochastic partial differential equations

  Prof. David Bolin, Statistics, KAUST

  Nov 14, 12:00 - 13:00

  B9 L2 H1 R2322

  statistics SPDEs

  The talk will give an overview of some recent developments of statistical models based on stochastic partial differential equations. We will in particular focus on equations with non-local differential operators or non-Gaussian driving noise, and explain when any why such models are useful. As motivating applications, analysis of longitudinal medical data and ocean waves will be considered.
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  Optimal Singular Value Thresholding in Correlated Noise

  Prof. David L. Donoho, Department of Statistics, Stanford University

  Nov 12, 15:00 - 16:00

  B19 Auditorium

  We consider the problem of recovering a low-rank signal matrix in the presence of a general, unknown additive noise; more specifically, noise where the eigenvalues of the sample covariance matrix have a general bulk distribution. We assume given an upper bound for the rank of the assumed orthogonally invariant signal, and develop a selector for hard thresholding of singular values, which adapts to the unknown correlation structure of the noise.
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  Deepnet Spectra and the Two Cultures of Data Science

  Prof. David L. Donoho, Department of Statistics, Stanford University

  Nov 12, 12:00 - 13:00

  B9 L2 H2 R2325

  A variety of intriguing patterns in eigenvalues were observed and speculated about in ML conference papers. We describe the work of Vardan Papyan showing that the traditional subdisciplines, properly deployed, can offer insights about these objects that ML researchers had.