Gaussian processes

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.