Events

May 26 - Jun 2, 2024

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  Deep Neural Networks for Large-scale Complex Spatial and Spatio-temporal Processes

  Pratik Nag , Ph.D. Student, Statistics

  May 29, 09:00 - 11:30

  B4 L5 R5220

  Environmental statistics play a critical role in various interconnected domains, encompassing weather and climate forecasting, air quality monitoring, and sustainable urban planning. However, because of their high inherent unpredictability and nonstationarity, modeling complex spatio-temporal dynamics of environmental processes is challenging. This dissertation develops a set of DNN based methods for large-scale spatial and spatio-temporal processes.

Mar 12 - Mar 19, 2023

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  On Inference About the Smoothness Parameter in Gaussian Matern Random Fields

  Prof. Victor DeOliveira, Professor in Department of Management Science and Statistics in the Carlos Alvarez College of Business

  Mar 15, 15:00 - 16:00

  B1 L4 R4102

  The Mat\'ern family of covariance functions is currently the most commonly used for the analysis of geostatistical data due to its ability to describe different smoothness behaviors. Yet, in many applications the smoothness parameter is set at an arbitrary value.

Nov 13 - Nov 20, 2022

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  Interval Prediction for Spatial Data Using Deep Learning Models

  Ghulam Qadir, Posdoctoral fellow, Computational Statistics group at Heidelberg Institute for Theoretical Studies, Germany

  Nov 17, 10:00 - 11:00

  B1 L4 R4102

  Statistical analysis for the purpose of prediction is preferably accompanied by uncertainty quantification, often in the form of prediction intervals. Deep learning approaches have been extensively shown to provide accurate point predictions in many applications.

May 30 - Jun 6, 2021

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

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

  May 31, 16:00 - 18:00

  KAUST

  The modeling of spatio-temporal and multivariate spatial random fields has been an important and growing area of research due to the increasing availability of space-time-referenced data in a large number of scientific applications. In geostatistics, the covariance function plays a crucial role in describing the spatio-temporal dependence in the data and is key to statistical modeling, inference, stochastic simulation, and prediction. Therefore, the development of flexible covariance models, which can accommodate the inherent variability of the real data, is necessary for advantageous modeling of random fields. This thesis is composed of four significant contributions in the development and applications of new covariance models for stationary multivariate spatial processes, and nonstationary spatial and spatio-temporal processes. Firstly, this thesis proposes a semiparametric approach for multivariate covariance function estimation with flexible specification of the cross-covariance functions via their spectral representations. The flexibility in the proposed cross-covariance function arises due to B-spline based specification of the underlying coherence functions, which in turn allows for capturing non-trivial cross-spectral features. The proposed method is applied to model and predict the bivariate data of particulate matter concentration and wind speed in the United States. Secondly, this thesis introduces a parametric class of multivariate covariance functions with asymmetric cross-covariance functions. The proposed covariance model is applied to analyze the asymmetry and perform prediction in a trivariate data of particulate matter concentration, wind speed and relative humidity in the United States.
 Thirdly, the thesis presents a space deformation method which imparts nonstationarity to any stationary covariance function. The proposed method utilizes the functional data registration algorithm and classical multidimensional scaling to estimate the spatial deformation. The application of the proposed method is demonstrated on precipitation data from Colorado, United States. Finally, this thesis proposes a parametric class of time-varying spatio-temporal covariance functions, which are stationary in space but nonstationary in time. The proposed time-varying spatio-temporal covariance model is applied to study the seasonality effect and perform space-time predictions in the daily particulate matter concentration data from Oregon, United States.

Nov 8 - Nov 15, 2020

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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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  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.

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

Nov 17 - Nov 24, 2019

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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 3 - Nov 10, 2019

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  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.

Nov 11 - Nov 18, 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.

Jun 11 - Jun 18, 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.

Apr 30 - May 14, 2017

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  Heteroscedastic BART Using Multiplicative Regression Trees

  Matthew Pratola, Assistant Professor of Statistics, The Ohio State University

  May 7, 16:00 - 17:00

  B1 L4 R4102

  Ohio State University Environmental Statistics

  Bayesian additive regression trees (BART) has become increasingly popular as a flexible and scalable non-parametric model useful in many modern applied statistics regression problems. It brings many advantages to the practitioner dealing with large and complex non-linear response surfaces, such as a matrix-free formulation and the lack of a requirement to specify a regression basis a priori.

Mar 6 - Mar 20, 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 14, 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 15, 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.

Sep 27 - Oct 11, 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 15, 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.

Nov 23 - Nov 30, 2014

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  Bayesian Regression Trees, Nonparametric Heteroscedastic Regression Modeling and MCMC Sampling

  Matthew Pratola, Assistant Professor of Statistics, The Ohio State University

  Nov 24, 15:00 - 16:00

  B1 L2

  nonparametric statistics Bayesian Statistics

  In this talk, we introduce a new Bayesian regression tree model that allows for possible heteroscedasticity in the variance model and devise novel MCMC samplers that appear to adequately explore the posterior tree space of this model.

Sep 7 - Sep 14, 2014

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  Uncertainty Quantification of Tsunami Models

  Serge Guillas, Professor of Statistics, University College London (UCL)

  Sep 8, 15:00 - 16:00

  B1

  uncertainty quantification Environmental Statistics

  In this talk, we first show various strategies for the efficient emulation of simulators having uncertain inputs, with applications to tsunami wave modelling. A fast surrogate of the simulator's time series of outputs is provided by the outer product emulator.