Monday, November 18, 2019, 00:00
- 23:45
Auditorium 0215, between building 2 and 3
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.
Roy Maxion, Research Professor, Computer Science Department, Carnegie Mellon University
Thursday, November 07, 2019, 09:00
- 10:00
TBD

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.

Registration is mandatory and will determine the time of the workshop (i) 9:00 AM - 10:00 AM or (ii) 4:00 PM - 5:00 PM. To register please click here.

Roy Maxion, Research Professor, Computer Science Department, Carnegie Mellon University
Wednesday, November 06, 2019, 09:00
- 10:00
TBD

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.

Registration is mandatory and will determine the time of the workshop (i) 9:00 AM - 10:00 AM or (ii) 4:00 PM - 5:00 PM. To register please click here.

Prof. William Kleiber, Associate Professor of Applied Mathematics, University of Colorado, USA
Tuesday, November 05, 2019, 14:00
- 15:00
Building 1, Level 4, Room 4102
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.
Roy Maxion, Research Professor, Computer Science Department, Carnegie Mellon University
Monday, November 04, 2019, 09:00
- 10:00
TBD

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.

Registration is mandatory and will determine the time of the workshop (i) 9:00 AM - 10:00 AM or (ii) 4:00 PM - 5:00 PM. To register please click here.

Thursday, September 26, 2019, 12:00
- 13:00
Building 9, Level 2, Hall 1, Room 2322
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.
Thursday, September 12, 2019, 12:00
- 13:00
Building 9, Level 2, Lecture Hall 1
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.
Dr. Jos Lenders, Deputy Editor, Advanced Materials, Wiley
Tuesday, July 09, 2019, 14:00
- 15:00
B3 L5 Room 5209
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?
Prof. Liching Chiu, Graduate Program of Teaching Chinese as a Second Language (TCSL), National Taiwan University
Tuesday, July 02, 2019, 10:00
- 11:00
B3 L5 Room 5209
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.
Dr. Luigi Lombardo, University of Twente, Netherlands
Tuesday, May 14, 2019, 16:00
- 17:00
B1 L4 Room 4102
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.
Thursday, May 09, 2019, 12:00
- 13:00
B9 L2 Lecture Hall 1
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.
Thursday, May 02, 2019, 12:00
- 13:00
B9 L2 Hall 1
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.
Prof, David Stoffer, University of Pittsburgh, Pennsylvania, USA
Friday, April 26, 2019, 15:00
- 18:00
B1 L4 Room 4102
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). 
Prof. Daniel Peña Sánchez de Rivera, Department of Statistics, Universidad Carlos III de Madrid
Thursday, April 25, 2019, 16:00
- 17:00
B1 L4 Room 4102
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.
Thursday, April 25, 2019, 12:00
- 13:00
B9 L2 Lecture Hall 1
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.
Professor Ngai Hang Chan, Professor of Statistics, Chinese University of Hong Kong
Tuesday, April 23, 2019, 16:00
- 17:00
B1 L4 room 4102
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.
Thursday, April 18, 2019, 12:00
- 13:00
B9 L2 Hall 1
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.
Thursday, April 11, 2019, 12:00
- 13:00
B9 L2 Lecture Hall 1
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.