Gaussian processes

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

Efficient and Accurate Inference for Matérn Gaussian Processes on Intervals

1 min read ·
A new study by researchers at KAUST introduces a breakthrough method for statistical modeling with Matérn Gaussian processes, a popular tool in spatial statistics, machine learning, and the natural sciences. While Gaussian processes with stationary Matérn covariance functions (Matérn processes) are valued for their flexibility, their use with large datasets has been severely limited by the high computational cost of standard methods. The team has developed the first generally applicable approach that enables fast, linear-cost inference and prediction for Matérn processes on bounded intervals

Mapping risk on networks: A scalable model for spatial point processes earns international recognition

2 min read ·
KAUST Ph.D. student Damilya Saduakhas wins Best Paper Award for a new model mapping accident risk on complex road networks.

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.

CEMSE New Faculty: Joaquín Ortega Sánchez, Instructional Professor, Statistics

1 min read ·
Joaquín Ortega Sánchez is an instructional professor of statistics who joins the KAUST CEMSE Division after spending the last sixteen years working at the Mathematics Research Center (CIMAT) in Guanajuato, Mexico. Born in Venezuela, Ortega completed his postgraduate and graduate studies in London, where he studied mathematics at King’s College London, before obtaining his Ph.D. in Probability Theory across the city at Imperial College London. After his time in the U.K., he returned to his native Venezuela where he worked for over 20 years at the Universidad Central de Venezuela, Caracas. Over the course of his career, Ortega’s research work has focused on stochastic processes, specifically Gaussian processes and time series with applications in oceanography and biostatistics. And more recently his work has focused on functional data analysis.

Meet KAUST Prospective Student: Amin Wu

1 min read ·
Amin Wu is a 24-year-old graduate who will join KAUST from the Communication University of China. Wu will join the University in the fall of 2020 as an M.S./Ph.D. candidate in the KAUST Environmental Statistics Research Group under the supervision of Professor Ying Sun. Wu wants to be a teacher or researcher and use her expertise and knowledge to make the world a better place.

Maurizio Filippone

Associate Professor, Statistics
KAUST Associate Professor of Statistics Maurizio Filippone’s primary research focus is Bayesian statistics, namely, enabling sound decision-making through uncertainty quantification in model parameters and predictions. His main research interests are in models based on deep learning and Gaussian processes

Joaquin Ortega Sanchez

Instructional Professor, Statistics
Joaquín Ortega Sánchez is an instructional professor of statistics whose work has focused on stochastic processes, specifically Gaussian processes and time series, and more recently, functional data analysis.

Amin Wu

Amin Wu is an M.S./Ph.D. candidate in the KAUST Environmental Statistics Research Group under the supervision of Professor Raul Tempone. Before joining KAUST, Wu obtained a bachelor's degree from the Communication University of China. Research Interests Wu's research interests include the Spatio-temporal series and Gaussian processes.