Profiles

Raphaël Huser is an Associate Professor of Statistics and the principal investigator of the Extreme Statistics (XSTAT) research group. He is also affiliated with the Applied Mathematics and Computational Science (AMCS) Program.

Professor Huser received his Ph.D. in Statistics in 2013 from the Swiss Federal Institute of Technology, Switzerland, under the supervision of Professor Anthony C. Davison. He also holds a B.S. in Mathematics and an M.S. in Applied Mathematics from École polytechnique fédérale de Lausanne (EPFL), Switzerland.

After completing his Ph.D., Huser joined KAUST as a postdoctoral research fellow in January 2014. He was appointed Assistant Professor in March 2015 and promoted to Associate Professor of Statistics in 2022.

Raphaël Huser’s research primarily focuses on statistics of extreme events and risk assessment, including developing specialized statistical models with appealing statistical properties. Additionally, he studies efficient machine learning methods designed for massive datasets from complex spatio-temporal processes.

Huser’s novel methodological contributions are motivated and inspired by a wide variety of real data applications, which include the modeling of natural hazards in climate and earth sciences (e.g., heavy rainfall, heat waves, extreme sea surface temperatures, strong wind gusts, devastating landslides), the assessment of financial risk (e.g., turbulence in stock markets or cryptomarkets), and the characterization of brain signals during extreme stimuli (e.g., epileptic seizures).

Beyond creating new models with interesting statistical properties, one crucial aspect is fitting these complex models to big data. A critical area of Huser's current research is developing general-purpose, likelihood-free, fast and statistically efficient neural Bayes estimators. Being deeply anchored in statistical decision theory and relying on advanced deep-learning techniques, which makes them attractive both from a theoretical and a computational perspective, these estimators truly provide a paradigm shift challenging traditional statistical inference techniques for complex models with intractable likelihoods.

Huser, with collaborators, is contributing extensively to the early development of such estimators and their application to spatial (e.g., extreme) and multivariate models.

Matthew Sainsbury-Dale is a Research Scientist in the Statistics Program at King Abdullah University of Science and Technology (KAUST), where he works in the Extreme Statistics (XSTAT) research group led by Associate Professor Raphaël Huser. His research sits at the interface of statistics and machine learning, with a focus on spatial and spatio-temporal statistics and the use of deep learning for fast, simulation-based inference in complex statistical models.

Matthew places a strong emphasis on open source software that makes new statistical methods readily available in practice. He is the lead developer of the package NeuralEstimators, available in Julia and R for efficient simulation-based inference using neural networks. He is also a co-developer of the R package FRK for modeling big spatial and spatio-temporal data.

Matthew obtained his Bachelor of Mathematics (Honours First Class) in 2019 and his Ph.D. in Statistics in 2024 from the University of Wollongong (UOW), Australia, under the supervision of Associate Professor Andrew Zammit-Mangion and Distinguished Professor Noel Cressie. His thesis, "Efficient Inference for Spatial and Spatio-Temporal Statistical Models Using Basis-Function and Deep-Learning Methods", received the Examiners Commendation for Outstanding Thesis and the Faculty of Engineering and Information Sciences Postgraduate Thesis Award. The Statistical Society of Australia recognized his doctoral work with three awards: the JB Douglas Award for Excellence in Postgraduate Research, the Tjanpi Award for Best Student Paper in Environmental Statistics, and the Venables Award for New Developers of Open Source Software for Data Analytics.

Matthew Sainsbury-Dale's research focuses on statistical deep learning, simulation-based inference, spatial and spatio-temporal statistics, and the development of efficient statistical software.

Donia Besher obtained her bachelor's degree in Mathematics from Sorbonne University, France. She is now a master's student in Statistics at King Abdullah University of Science and Technology (KAUST), working under Prof. Raphaël Huser's supervision.

Donia is interested in deep learning for time series forecasting, spatiotemporal modeling, and financial time series analysis.

Paolo Redondo obtained his B.S. and M.S. degrees in Statistics from the University of the Philippines Diliman. He is a member of the Biostatistics and Extreme Statistics research groups.

Paolo's research focus on developing methodologies to characterize nonlinear dependence in brain networks and to understand the tail behavior of brain dynamics during abnormal events such as epileptic seizures.

Paolo Redondo obtained his B.S. and M.S. degrees in Statistics from the University of the Philippines Diliman. He is a member of the Biostatistics and Extreme Statistics research groups.

Paolo's research focus on developing methodologies to characterize nonlinear dependence in brain networks and to understand the tail behavior of brain dynamics during abnormal events such as epileptic seizures.

Simon Nik is an Instructional Assistant Professor of Statistics in the Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division at KAUST. His research lies at the intersection of time-series analysis, stochastic modeling, and statistical methodology. He develops statistical methods for discrete and nonnegative data and studies complex systems involving uncertainty and dependence.

Dr. Nik joined KAUST in December 2024 as a postdoctoral research fellow in the Extreme Statistics (XSTAT) research group. Before joining KAUST, he was a scientific employee at Helmut Schmidt University Hamburg, Germany, where he completed his doctorate in Mathematics and Statistics in 2024. His dissertation focused on the stochastic modeling and statistical analysis of nonnegative data and time series.

Alongside his research, Dr. Nik has 14 years of teaching experience in mathematics and statistics.

Professor Nik’s research focuses on stochastic modeling and statistical inference for discrete and nonnegative data, with a particular emphasis on discrete time series. His more recent research interests include dynamical systems and their applications to financial markets and coral-reef dynamics.