Events

Mar 8 - Mar 14, 2020

• 

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

  Mar 12, 12:00 - 13:00

  KAUST

  functional data analysis spatio-temporal statistics

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

Mar 1 - Mar 7, 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 22, 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 8, 2020

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  Mathematical modeling of the stochastic climate system

  Georgiy L. Stenchikov, Professor (former), 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.
• 

  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 - Nov 30, 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
• 

  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
• 

  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 23, 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 16, 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.
• 

  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.
• 

  EE 398 Graduate Seminar

  Tareq Al-Naffouri, Professor, Electrical and Computer Engineering

  Nov 10, 12:00 - 13:00

  B9 L2 H1 R2322

  Tareq Al-Naffouri is a professor of Electrical Engineering (EE) and Principale investigator of the Information System Lab (ISL). He is also an active member of the Sensor Initiative (SI) at the King Abdullah University of Sciences and Technology, Saudi Arabia.

Nov 3 - Nov 9, 2019

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  On mean-field game price models

  Diogo Gomes, Program Chair, Applied Mathematics and Computational Sciences

  Nov 7, 12:00 - 13:00

  B9 L2 H1 R2322

  mean-field games price formation modeling linear-quadratic problem optimal transport with constraints supply and demand balance

  Abstract In this talk, we discuss a mean-field game price formation model. This model describes a large number of rational agents that can trade a commodity with an exogenous supply. The price is determined by a balance condition between supply and demand. We discuss the well-posedness of the model, the uniqueness and regularity of the price function. Then, we examine two explicit models - the linear-quadratic problem and a model with finitely many agents. Time permitting, we will examine the connections between this problem and optimal transport with constraints. Brief Biography Diogo Gomes
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  Reproducibility in Computer Science - Part 3

  Roy Maxion, Research Professor, Computer Science Department, Carnegie Mellon University

  Nov 6, 16:00 - 17:00

  B9 L3 R3223

  reproducibility issues reliability testing Computer science complex models artificial intelligence machine learning scientific enterprise

  Roy Maxion will give three lectures focusing broadly on different aspects of an increasingly important topic: reproducibility. Reproducibility tests the reliability of an experimental result and is one of the foundations of the entire scientific enterprise. We often hear that certain foods are good for you, and a few years later we learn that they're not. A series of results in cancer research was examined to see if they were reproducible. A startling number of them - 47 out of 53 - were not. Matters of reproducibility are now cropping up in computer science, and given the importance of computing in the world, it's essential that our own results are reproducible -- perhaps especially the ones based on complex models or data sets, and artificial intelligence or machine learning. This lecture series will expose attendees to several issues in ensuring reproducibility, with the goal of teaching students (and others) some of the crucial aspects of making their own science reproducible. Hint: it goes much farther than merely making your data available to the public.
• 

  Reproducibility in Computer Science - Part 2

  Roy Maxion, Research Professor, Computer Science Department, Carnegie Mellon University

  Nov 5, 16:00 - 17:00

  B9 L3 R3223

  reproducibility issues reliability testing Computer science complex models artificial intelligence machine learning scientific enterprise

  Roy Maxion will give three lectures focusing broadly on different aspects of an increasingly important topic: reproducibility. Reproducibility tests the reliability of an experimental result and is one of the foundations of the entire scientific enterprise. We often hear that certain foods are good for you, and a few years later we learn that they're not. A series of results in cancer research was examined to see if they were reproducible. A startling number of them - 47 out of 53 - were not. Matters of reproducibility are now cropping up in computer science, and given the importance of computing in the world, it's essential that our own results are reproducible -- perhaps especially the ones based on complex models or data sets, and artificial intelligence or machine learning. This lecture series will expose attendees to several issues in ensuring reproducibility, with the goal of teaching students (and others) some of the crucial aspects of making their own science reproducible. Hint: it goes much farther than merely making your data available to the public.
• 

  Mixed graphical-basis models for large nonstationary and multivariate spatial data problems

  Dr. William Kleiber, Associate Professor of Applied Mathematics, University of Colorado, USA

  Nov 5, 14:00 - 15:00

  B1 L4 R4102

  multivariate spatial processes statistical climatology and statistics for energy sciences

  In this talk, we explore a graphical model representation for the stochastic coefficients relying on the specification of the sparse precision matrix. Sparsity is encouraged in an L1-penalized likelihood framework. Estimation exploits a majorization-minimization approach. The result is a flexible nonstationary spatial model that is adaptable to very large datasets.
• 

  Reproducibility in Computer Science - Part 1

  Roy Maxion, Research Professor, Computer Science Department, Carnegie Mellon University

  Nov 4, 16:00 - 17:00

  B9 L3 R3223

  reproducibility issues reliability testing Computer science complex models artificial intelligence machine learning scientific enterprise

  Roy Maxion will give three lectures focusing broadly on different aspects of an increasingly important topic: reproducibility. Reproducibility tests the reliability of an experimental result and is one of the foundations of the entire scientific enterprise. We often hear that certain foods are good for you, and a few years later we learn that they're not. A series of results in cancer research was examined to see if they were reproducible. A startling number of them - 47 out of 53 - were not. Matters of reproducibility are now cropping up in computer science, and given the importance of computing in the world, it's essential that our own results are reproducible -- perhaps especially the ones based on complex models or data sets, and artificial intelligence or machine learning. This lecture series will expose attendees to several issues in ensuring reproducibility, with the goal of teaching students (and others) some of the crucial aspects of making their own science reproducible. Hint: it goes much farther than merely making your data available to the public.

Oct 13 - Oct 19, 2019

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  Monte Carlo Computation

  Oct 17, 12:00 - 13:00

  B9 L2 H1 R2322

  Abstract Abstract text. Brief Biography Ajay Jasra received his Ph.D. degree in statistics from Imperial College London in 2005. Since 2011 he has been a tenured associate professor at the Department of Statistics and Applied Probability at the National University of Singapore (NUS). Between 2005-2008 he has held various post-doctoral positions at the University of Oxford, the University of Cambridge and the Institute of Statistical Mathematics in Tokyo. He was also Chapman Fellow of Mathematics at Imperial College London in that period. Between 2008-2011 he was assistant professor, with

Oct 6 - Oct 12, 2019

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  Modeling, studying, and forecasting the circulation and Climate of the Red Sea

  Ibrahim Hoteit, Professor, Earth Science and Engineering

  Oct 10, 12:00 - 13:00

  B9 L2 H1 R2322

  Abstract The talk will present the integrated data-driven modeling system that we are developed to study and predict the circulation and the climate of the Red Sea. I will first describe the modeling system and summarize our key findings on the Red Sea general circulation, including its dominant eddy activity, the striking seasonally overturning circulation, and the occasional northern deep water formation events, and discuss their impact on the Red Sea ecosystem. I will then focus on our efforts to develop a regional ensemble data assimilation and forecasting system, presenting recent

Sep 22 - Sep 28, 2019

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  Statistics of Extremes

  Raphaël Huser, Associate Professor, Statistics

  Sep 26, 12:00 - 13:00

  B9 L2 H1 R2322

  extreme environmental events probabilities risks univariate extremes Spatial extremes

  Extreme environmental events such as droughts, floods and heat-waves take place in space and time, and it is necessary to take this into account when evaluating their risks and estimating their probabilities. During this seminar, I will review some classical and more recent work on this topic, focusing on the modeling of univariate and spatial extremes. The ideas will be illustrated by applications to peak river flow data from the UK, and heavy rainfall close to Jeddah.

Sep 8 - Sep 14, 2019

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  Waves in Complex Media: Theories and Applications

  Ying Wu, Professor, Applied Mathematics and Computational Sciences

  Sep 12, 12:00 - 13:00

  B9 L2 H1

  We focus on the theoretical modeling and numerical simulation of classical wave propagation in complex systems, such as periodic structures and random media. In this talk, I will give an overview of the research conducted in our group by emphasizing on three major aspects: numerical method, homogenization, and applications in artificial materials.

Jul 7 - Jul 13, 2019

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  Publishing Advanced Materials Science – How to maximize your success!

  Dr. Jos Lenders, Deputy Editor, Advanced Materials, Wiley

  Jul 9, 14:00 - 15:00

  B3 L5 R5209

  materials science wiley advanced materials

  Materials science is a multidisciplinary field of research with many different scientists and engineers having various backgrounds active in it. The literature landscape consequently is populated currently by a wide range of journals which greatly differ in purpose, scope, quality, and readership. Jos Lenders, Deputy Editor of Advanced Materials, Advanced Functional Materials, and Advanced Optical Materials, will track some of the most important developments and trends in the research field and the Advanced journals program. Last year, Advanced Materials reached an Impact Factor of 21.95 and received over 8,300 submissions – and Advanced Functional Materials over 9,200. Only around 15% of all those papers made it to publication in the journal, and this rate is similar for all other Advanced journals. So, what do editors do to select the very best papers, and what can authors do to optimize their chances of having their manuscripts accepted?

Jun 30 - Jul 6, 2019

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  Abstract Writing

  Prof. Liching Chiu, Graduate Program of Teaching Chinese as a Second Language (TCSL), National Taiwan University

  Jul 2, 10:00 - 11:00

  B3 L5 R5209

  Scientific Writing

  This series of lectures guide students to the preparation and analysis of a well-organized abstract. We will discuss the proper language (tense, voice, and person) for abstract writing, and learn how to meet the purposes of different abstracts. Finally, students will have a chance to compose and evaluate their writing. Topics: Overview of abstract writing; Conference abstract journal abstract; Organization of an abstract; Language conventions of abstract writing; Disciplinary abstract analysis; Frequent mistakes of abstract writing.
• 

  Physical Layer Security of Wireless-powered NOMA-based Cognitive Radio Networks: Fundamentals & Challenges

  Dr. Faissal El Bouanani, Chair of CommNet and ACOSIS conferences

  Jul 1, 11:00 - 12:00

  B1 L2 R2202

  Cognitive radio network IoT 5G and beyond

  Abstract In the last decade, with the emergence of the internet of things (IoT) as well as machine-to-machine (M2M) paradigms, future wireless networks are targeting new critical services such as remote healthcare, surveillance, transportation, etc. In this regard, with the massive expected number of connected IoT and M2M devices, several requirements have been imposed by the regulatory bodies onto the deployment of 5G network. These requirements include, but not limited to, higher connectivity, reduced latency, as well as increased spectral and energy efficiency. Interestingly, the

Jun 23 - Jun 29, 2019

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  5G IoT Networks: from Massive access to URLLC

  Prof. Yonghui Li, Director of Wireless Engineering Laboratory, The University of Sydney

  Jun 24, 16:30 - 17:30

  B1 L3 R3119

  the university of sydney IoT M2M Communication

  Connected smart objects, platforms and environments have been identified as the next big technology development, enabling significant society changes and economic growth. The entire physical world will be connected to the Internet. The intelligent network for automatic interaction and processing between objects and environments, referred to as the Machine to Machine Communications (M2M) for Internet of Things (IoT), will become an inherent part of areas such as electricity, transportation, industrial control, utilities management, healthcare, water resources management and mining. Wireless networks are one of the key enabling technologies of the IoT. They are likely to be universally used for last mile connectivity due to their flexibility, scalability and cost effectiveness.

May 26 - Jun 1, 2019

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  Non-Orthogonal Multiple Access for 5G and Beyond

  Prof. Daniel Costa , Federal University of Ceará

  May 26, 14:00 - 15:00

  B1 L2 R2202

  Federal University of Ceará 5th generation cellular

  Non-orthogonal multiple access (NOMA) has recently emerged not only as a new design of multiple access techniques in cellular networks, but also as a general principle of network architecture for applications beyond cellular systems. This talk will present and discuss the fundamentals of NOMA, and examine how it can be combined with other emerging communication technologies. Some new research trends and challenges will also be discussed.

May 12 - May 18, 2019

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  The strength of multidisciplinary research: the joint example of statistics and geoscience

  Dr. Luigi Lombardo, University of Twente, Netherlands

  May 14, 16:00 - 17:00

  B1 L4 R4102

  University of Twente spatial statistics

  Different scientific branches have the potential to develop topics which would provide visibility and fame. However, comparable if not greater milestones can be achieved when researchers from totally different fields join their efforts. This seminar will summarize the scientific journey of a former member of KAUST, which spent three years here as a postdoc in statistics coming from a pure geological background, combining the best out of the two worlds. Examples of the latest researches will be provided in the context of space, time and space-time statistics, bridging it with the underlying geoscientific research questions.

May 5 - May 11, 2019

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  Joint longitudinal-survival models using R-INLA

  Janet van Niekerk, Research Scientist, Statistics

  May 9, 12:00 - 13:00

  B9 L2 H1

  stochastic smoothing pipeline R-INLA

  Joint models have received increasing attention during recent years with extensions into various directions; numerous hazard functions, different association structures, linear and non-linear longitudinal trajectories amongst others. They gained popularity amongst practitioners by the ability to incorporate various data sources. In this talk, we will introduce joint models and provide some conceptual ideas about their use and necessity. Also, we will illustrate how these models can be formulated as Latent Gaussian Models and hence be implemented using R-INLA.

Apr 28 - May 4, 2019

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  Estimating the expected information gain in Bayesian optimal experimental design

  May 2, 12:00 - 13:00

  B9 L2 H1

  optimization DLMC DLMCIS

  Optimal experimental design for parameter estimation is a fast-growing area of research. Let us consider the experimental goal to be the inference of some attributes of a complex system using measurement data of some chosen system responses, and the optimal designs are those that maximize the value of measurement data. The value of data is quantified by the expected information gain utility, which measures the informativeness of an experiment. Often, a mathematical model is used that approximates the relationship between the system responses and the model parameters acting as proxies for the attributes of interest.

Apr 21 - Apr 27, 2019

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  Almost everything you always wanted to know about NONLINEAR STATE SPACE MODELS (but were afraid to ask)

  Prof, David Stoffer, University of Pittsburgh, Pennsylvania, USA

  Apr 26, 15:00 - 18:00

  B1 L4 R4102

  university of pittsburgh linear guassian model Numerical Optimization

  Ever wonder why, when you fly to Jeddah you don't end up in Riyadh? The tracking devices use a nonlinear state space model to make sure your plane is on course. While inference for the linear Gaussian model is fairly simple, inference for nonlinear models can be difficult and often relies on derivative free numerical optimization techniques. A promising method that I will discuss is based on particle approximations of the conditional distribution of the hidden process given the data. This distribution is needed for both classical inference (e.g., Monte Carlo EM type algorithms) and Bayesian inference (e.g., Gibbs sampler).
• 

  Forecasting Multiple Time Series with One-Sided Dynamic Principal Components

  Prof. Daniel Peña Sánchez de Rivera, Department of Statistics, Universidad Carlos III de Madrid

  Apr 25, 16:00 - 17:00

  B1 L4 R4102

  Time Series big data

  Generalized Dynamic principal components are presented and it is shown how to define one side inear combinations of the present and past values of the series that minimize the reconstruction mean squared error (ODPC). It is shown that the ODPC introduced in this paper can be successfully used for forecasting high-dimensional multiple time series.
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  Applications of random matrix theory for wireless communication and big data processing

  Abla Kammoun, Senior Research Scientist, Electrical and Computer Engineering

  Apr 25, 12:00 - 13:00

  B9 L2 H1

  Since the pioneer works of Telatar, random matrix theory has found a variety of applications in engineering disciplines that, to name a few, include wireless communication and signal processing. Its scope is now going far beyond the field of mathematics, being recognized as an indispensable tool for advanced research in engineering disciplines as can be evidenced by the dramatic increase in related publications. Recently, random matrix theory has found its way into the field of big data processing, allowing accurate characterization of the performance of many algorithms met in the field of machine learning.
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  Inference for Spatial Trends

  Professor Ngai Hang Chan, Professor of Statistics, Chinese University of Hong Kong

  Apr 23, 16:00 - 17:00

  B1 L4 R4102

  Non-stationary spatial models are widely applicable in diverse disciplines, ranging from bio-medical sciences to geophysical studies. In many of theses applications, testing for structural changes in the trend and testing the specific form of the trend are highly relevant. A novel statistics based on a discrepancy measure over small regions is proposed in this paper. Such a measure can be used to construct tests for structural trends and to identify change boundaries of the trends. By virtue of the m-dependence approximation of a stationary random eld, asymptotic properties and limit distributions of these tests are established. The method is illustrated by simulations and data analysis.

Apr 14 - Apr 20, 2019

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  Estimation Problems for Partial and Fractional Differential Equations - Spring 2019

  Apr 18, 12:00 - 13:00

  B9 L2 H1

  Fractional differential equation Partial Differential Equations

  We will present some new methods for source and parameters estimation for partial and fractional differential equations and illustrate the results with some simulations and real applications.

Apr 7 - Apr 13, 2019

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  Space-time conservation element and solution element (CESE) method for computational fluid dynamics

  Hua Shen

  Apr 11, 12:00 - 13:00

  B9 L2 H1

  computational fluid dynamics ​CFD

  Space-time conservation element and solution element (CESE) method is a unique finite-volume-type method for computational fluid dynamics (CFD). This approach has several attractive properties, including: (i) unified treatment of the space and time such that only one step is required to construct high-order schemes; (ii) a highly compact stencil regardless of the order of the accuracy; (iii) easiness of extension to any arbitrary shape of polygonal elements. Since its inception, the CESE method has achieved great success in different areas.

Mar 31 - Apr 6, 2019

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  High-dimensional Q-learning for Dynamic Treatment Regimes

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

  Mar 31, 12:00 - 13:00

  B3 L5 R5220

  HQ-learning statistical inference data analysis simulation

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

Mar 10 - Mar 16, 2019

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  Truth, Knowledge, P-Values, Bayes, & Inductive Inference

  Edsel A. Peña, iProfessor in the Department of Statistics, University of South Carolina

  Mar 14, 16:00 - 17:00

  B1 L4 R4102

  statistics Bayesian computational statistics inductive inference

  The talk will concern the role of statistical thinking in the search for Truth. This will bring us to a discussion of P-values, which has been, and still is, a central concept in Statistics and is a critical and highly controversial tool of scientific research. Recently it has elicited much, sometimes heated, debates and discussions. The American Statistical Association (ASA) was even compelled to release an official statement in early March 2016 regarding this issue, and a psychology journal gone to the extreme of banning the use of P-values in articles appearing in its journal!

Nov 11 - Nov 17, 2018

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  2018 Workshop on Statistics and Data Science

  Nov 12, 08:30 - Nov 15, 17:00

  Auditorium 0215 between Buildings 4 & 5

  data science high dimensional statistics modeling biological processes physical processes

  The Statistics Program at KAUST is proud to host the 2018 Workshop on Statistics and Data Science. This workshop gathers the leading experts on statistical data science to discuss the current needs, challenges and opportunities of modeling massive and high dimensional data, predicting complex biological and physical processes. The workshop will run from November 12-14. Talks and posters presentations will take place in Auditorium 0215 (between Buildings 4 & 5). View Workshop's Agenda.

Nov 4 - Nov 10, 2018

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  Spatio-Temporal Data Analysis by Transformed Gaussian Processes

  Yuan Yan, PhD Student of Prof. Marc Genton, KAUST

  Nov 5, 17:15 - 18:15

  B2 L5 R5220

  spatio-temporal data analysis statistics

  In the analysis of spatio-temporal data, statistical inference based on the assumption that the data follow a Gaussian distribution is ubiquitous due to its many attractive properties. However, data collected from different fields of science, such as meteorology, astronomy, biology and medicine, rarely meet the assumption of Gaussianity. One option is to apply a monotonic transformation to the data such that the transformed data have a distribution that is close to Gaussian. In this thesis, we focus on a flexible two-parameter family of transformations, the Tukey g-and-h (TGH) transformation

Mar 18 - Mar 24, 2018

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  Computational and Statistical Interface to Big Data

  Xin Gao, Program Chair, Computer Science

  Mar 19, 08:00 - Mar 21, 17:00

  B9 L2 H2

  We are now in the fourth paradigm of science: Data Science. The massive amount of structured and unstructured data has posed new challenges and opportunities to the fields of computer science and statistics. Traditional computational and statistical methods for data storage, curation, sharing, querying, updating, visualization, analysis, and privacy have been shown to fail in the big data scenario due to the unprecedented volume, velocity, variety, veracity and value of the big data. This conference will bring together a number of prominent researchers in Computer Science and Statistics with common interests and active research in big data, as well as the researchers at KAUST who regularly generate or face big data, such as those in bioscience and red sea research.

Jun 11 - Jun 17, 2017

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  Computational Methods for Large Spatio-temporal Datasets and Functional Data Ranking

  Jun 13, 09:00 - 10:00

  B1 L4 R4102

  computational statistics Environmental Statistics

  In this thesis defense, I will talk about two topics—computational methods for large spatial datasets and functional data ranking. Both are tackling the challenges of big and high-dimensional data.

May 22 - May 28, 2016

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  Welcome at Kaust UQ School On Numerical Methods For Direct And Inverse Problems 2016 22-28 May

  Raul Tempone, Professor, Applied Mathematics and Computational Sciences

  May 22, 12:00 - May 28, 12:00

  B9 H1 R2322

  numerical methods stochastic differential equations statistics

  KAUST UQ SCHOOL 2016 is an annual base thematic conference at King Abdullah University of Science and Technology held by Raul Tempone, Professor of Applied Mathematics and Computational Sciences at CEMSE (Computer, Electrical and Mathematical Sciences & Engineering Division). Tempone’s interests in the mathematical foundation of computational science and engineering are reflected in this summer school. The school’s goal is to provide participants with an overview on the most recent research progress in the field of uncertainty quantification, with emphasis to • Multi-Level and Multi-Index

Mar 13 - Mar 19, 2016

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  Workshop on Statistical Process Monitoring and Risk Assessment for Engineering and Spatial Environmental Applications

  Mar 13, 09:00 - Mar 15, 15:00

  B1 L4 R4102

  Environmental Statistics spatial statistics

  LIST OF SPEAKERS KAUST Environmental Statistics Group, CEMSE Division - Ying Sun (ying.sun@kaust.edu.sa), PI - Fouzi Harrou (fouzi.harrou@kaust.edu.sa), Postdoc - Huang Huang (huang.huang@kaust.edu.sa), PhD Student - Tianbo Chen (tianbo.chen@kaust.edu.sa), PhD Student - Rui Meng (rui.meng@kaust.edu.sa), Master Student - Sulaiman Binkhamis (sulaiman.binkhamis@kaust.edu.sa), Master Student Hydrology and Land Observation Group, BESE Division - Gaohong Yin (gaohong.yin@kaust.edu.sa), Master Student Collaborators - NorEddine Ghaffour (noreddine.ghaffour@kaust.edu.sa), co-I, WDRC - Matthew McCabe

Feb 7 - Feb 13, 2016

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  A Distributed Implementation of the Multi-resolution Approximation for Very Large Spatial Data

  Dorit Hammerling, National Center for Atmospheric Research (NCAR)

  Feb 10, 15:30 - 16:30

  B1 L4 R4102

  spatial statistics

  With data of rapidly increasing sizes in the environmental and geosciences such as satellite observations and high-resolution climate model runs, the spatial statistics community has recently focused on methods that are applicable to very large data. One such state-of-the-art method is the multi-resolution approximation (MRA), which was specifically developed with high performance computer architecture in mind.

Nov 8 - Nov 14, 2015

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  Disease Risk Estimation by Combining Case-Control Data with Aggregated Information on the Population at Risk

  Xiaohui Chang, Assistant Professor, College of Business at Oregon State University

  Nov 9, 15:30 - 16:00

  B1 L4 R4102

  statistics

  We propose a novel statistical framework by supplementing case–control data with summary statistics on the population at risk for a subset of risk factors. Our approach is to first form two unbiased estimating equations, one based on the case–control data and the other on both the case data and the summary statistics, and then optimally combine them to derive another estimating equation to be used for the estimation.
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  Kriging Asymptotics

  William Kleiber, Assistant Professor, University of Colorado

  Nov 9, 15:00 - 16:30

  B1 L4 R4102

  spatial statistics

  Spatial analyses often focus on spatial smoothing using the geostatistical technique known as kriging. Theoretical results regarding large sample convergence rates of kriging predictors remain elusive. By casting kriging as a variational problem, we develop an equivalent kernel approximation technique that can also lead to computational feasibility for large data problems.

Oct 4 - Oct 10, 2015

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  Workshop on Computational Space-Time Statistics

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

  B1 L4 R4102

  Statistics of extremes Environmental Statistics

  Workshop on Computational Space-Time Statistics

Mar 8 - Mar 14, 2015

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  Collective Estimation of Multiple Bivariate Density Functions with Application to Angular-sampling-based Protein Structure Prediction

  Mehdi Moodaaliat, Assistant Professor, Marquette University

  Mar 10, 15:00 - 16:00

  B1

  statistics

  In this talk we develop a method for simultaneous estimation of density functions for a collection of populations of protein backbone angle pairs. Each log density function in the collection is modeled as a linear combination of a common set of basis functions. The shared basis functions are modeled as bivariate splines on triangulations and are estimated using data. The circular nature of angular data is taken into account by imposing appropriate smoothness constraints across boundaries of the triangles.