Bayesian Statistics

Bayesian Regression Trees, Nonparametric Heteroscedastic Regression Modeling and MCMC Sampling

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B1 L2

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.

Joint Posterior Inference for Latent Gaussian Models and extended strategies using INLA

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B3 L5 R5209

Bayesian inference is particularly challenging on hierarchical statistical models as computational complexity becomes a significant issue. Sampling-based methods like the popular Markov Chain Monte Carlo (MCMC) can provide accurate solutions, but they likely suffer a high computational burden.

New paper accepted in JGR - Earth Surface

1 min read ·
New accepted paper: Lombardo, L., Bakka, H., Tanyas, H., van Westen, C., Mai, P. M., and Huser, R. (2019+), Geostatistical modeling to capture seismic-shaking patterns from earthquake-induced landslides, Journal of Geophysical Research-Earth Surface [ PDF preprint]

CEMSE New Faculty: Ajay Jasra, Professor, Applied Mathematics and Computational Sciences

1 min read ·
Ajay Jasra is a professor of applied mathematics and computational sciences who recently joined the KAUST CEMSE Division in July 2019.

Yan Gong

Ph.D. Student, Statistics
Yan Gong was a Ph.D. student in Statistics at the King Abdullah University of Science and Technology (KAUST), under the supervision of Prof. Raphaël Huser. Yan successfully defended her PhD thesis entitled " Flexible Multivariate, Spatial, and Causal Models for Extremes" on March 28th, 2023; see her PhD thesis here. Her PhD committee was composed of Professors Raphaël Huser (chair), Valérie Chavez-Demoulin (external examiner from HEC Lausanne at UNIL, Switzerland), David Bolin, and Mohammed-Slim Alouini. For her next career steps, Yan has accepted a short-term postdoctoral research position at

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

Amanda Lenzi

Postdoctoral Research Fellow, Statistics
Amanda Lenzi is currently working as a Postdoctoral fellow under Mihai Anitescu, in Argonne National Laboratory, Chicago USA Amanda Lenzi was a Post-Doctoral fellow in Statistics at the King Abdullah University of Science and Technology (KAUST), under the supervision of Professor Marc G. Genton in his research group. Education and Early Carreer Amanda Lenzi completed her Ph.D. at the Technical University of Denmark (DTU) in 2017. She also holds a B.S. and an M.S. degree in Statistics from the University of Campinas (UNICAMP), Brazil. She joined KAUST in September 2017. Research Interest Her