applied statistics

excursions

excursions: A Framework for Probabilistic Excursion Sets and Contour Inference

rSPDE

rSPDE: A Computational Framework for Rational Approximations of Fractional Stochastic Partial Differential Equations

MetricGraph

MetricGraph: A Statistical Framework for Modeling Gaussian Fields on Metric Graphs

Mastering Network Spatial Statistics with the MetricGraph R Package

1 min read ·
A new open-access mini-course is expanding access to advanced network spatial statistics through practical training with the MetricGraph package R, a powerful tool developed by David Bolin (KAUST), Alexandre B. Simas (KAUST), and Jonas Wallin (Lund University) for modeling data on network structures such as road systems and river basins. Presented by Bolin and Simas at the University of Glasgow during the “ INLA: Past, Present, and Future” workshop, the mini-course provides step-by-step tutorials, real datasets, and comprehensive R code, all freely available online, opening new avenues for

Efficiency Assessment of Approximated Spatial Predictions for Large Datasets

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KAUST

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.

On The Coupling between HPC and Statistics: Challenges, Opportunities, and Future Trends of Emerging Techniques -- (Virtual SIAM CSE21 Minisymposium)

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KAUST

Abstract Big data modeling/inference and large-scale simulations have followed largely independent paths to the high-performance computing (HPC) frontier, but important opportunities now arise that can be addressed by combining the strengths of each. HPC is becoming increasingly significant in scaling existing statistical methods to larger and more complex applications and developing novel methods that are amenable to scaling within the constraints that exist in modern HPC architectures. The purpose of this minisymposium is to bring together researchers in the area of statistics and HPC to

High Performance Statistical Computing (HPSC): Challenges and Best Practices

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KAUST

************************************************** Organizers: Sameh Abdulah and Marc Genton ************************************************** Abstract Big data in terms of volume, intensity, and complexity is one of the main challenges to statisticians. The availability of different data sources has essential implications in collecting colossal data volumes that require other ways to be managed and analyzed. Existing analytical tools cannot be applied easily to big data volumes due to memory and computation constraints. Previously, statistical applications and traditional high performance

Solvers for Coupled Problems in Porous Media Science

Arne Naegel

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B9 L2 R2322

No abstract is available.

A new class of random field models for data on networks

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B9 L2 R2322 H1

Random fields are popular models in statistics and machine learning for spatially dependent data on Euclidian domains. However, in many applications, data is observed on non-Euclidian domains such as street networks. In this case, it is much more difficult to construct valid random field models. In this talk, we discuss some recent approaches to modeling data in this setting, and in particular define a new class of Gaussian processes on compact metric graphs.

Vasiliki Kordopati

Vasiliki Kordopati obtained Master degree in Computer Science under the co-supervision of Professor Boon Ooi at Photonics Laboratory and Prof. Xin Gao at Structural and Functional Bioinformatics Group (SFB) from King Abdullah University of Science and Technology (KAUST). Vasiliki is a co-founder of Oæsis, a King Abdullah University of Science and Technology (KAUST) startup spinout, where she is responsible for product development. A computer engineer by training, she received a bachelor's degree in Computer Engineering from the University of Patras, Greece. Research Interest Vasiliki's

Marc G. Genton

Al-Khawarzmi Distinguished Professor, Statistics
Professor Genton’s pioneering research on large-scale spatial and temporal data has profoundly affected the field of environmental statistics. He is an expert in spatial and spatio-temporal statistics with applications to environmental problems.

Grigory Malinovsky

Grigory Malinovsky is a M.S./Ph.D. candidate at the KAUST Optimization and Machine Learning Lab under the supervision of Professor Peter Richtarik. Before joining KAUST, Grigory obtained a bachelor's degree in applied mathematics and physics from the Moscow Institute of Physics and Technology, Russia. Research Interests Grigory's research interests include machine learning, optimization, and applied statistics. Specifically, he is interested in the theoretical aspects of optimization algorithms that can be applied to real machine-learning problems. Education Profile B.A., Major in Applied

Amine Bejaoui

Amine Bejaoui was an MS/Ph.D. student in the Communication Theory Lab (CTL) under the supervision of Professor Mohamed-Slim Alouini at King Abdullah University of Science and Technology (KAUST). Education and Early Career Amine Bejaoui obtained his engineering degree with majors in Economics and Scientific management from Ecole Polytechnique de Tunisie (EPT) , Tunisia, in 2015. Research Interest The scientific interests of Amine Bejaoui lies in the area of statistics, machine learning and the applications of Random Matrix Theory. Education Profile M.Sc. in Statistics, KAUST, Aug 2018 – Present