Low-probability-high-impact events affect us, as a society, more than ever before. In the Extreme Statistics Research Group, we develop novel statistical methodology and machine learning methods to better model, infer, understand, and ultimately predict these extreme events in a wide range of applications ranging from climate and earth sciences, to finance, and neuroscience.

Raphaël Huser is an Associate Professor of Statistics and the Principal Investigator of the Extreme Statistics (XSTAT; see this Youtube video overview) Research Group. He is also affiliated to the Applied Mathematics and Computational Science (AMCS) program.

Education and early career

Raphaël Huser received his Ph.D. degree in Statistics from the Swiss Federal Institute of Technology (EPFL) in 2013, working with Prof. Anthony C. Davison. He also holds a B.S. degree in Mathematics and an M.S. degree in Applied Mathematics from EPFL, Lausanne, Switzerland. After the Ph.D., he then worked as a Postdoctoral Research Fellow at KAUST for about a year from January, 2014, and was later appointed Assistant Professor in March, 2015, before transitioning to his current role as an Associate Professor of Statistics in 2022.

Areas of expertise and current scientific interests

Raphaël Huser’s research has primarily focused on statistics of extreme events and risk assessment, which includes the development of specialized statistical models with appealing statistical properties, as well as efficient machine learning methods, designed for massive datasets from complex spatio-temporal processes. Huser's novel methodological contributions are motivated and fed 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 important aspect is to be able to fit these complex models to big data, and one important area of Huser's current research efforts focuses on the development of general-purpose, likelihood-free, fast and statistically-efficient neural Bayes estimators. Being deeply anchored into 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 developments of such estimators and their application to spatial (e.g., extreme) or multivariate models.

Honors and Awards

  • 2022: Laureate of the Abdel El-Sharaawi Early Investigator Award, from the The Environmetrics Society (TIES), association of the International Statistical Institute (ISI); click here for more details. The award citation reads:
    In recognition of the fundamental contributions to the statistics of extremes, particularly, the development of new subasymptotic models for spatial extremes and new statistical models for high-impact and rare events in univariate, multivariate, spatial, and spatiotemporal settings, in order to solve key methodological and applied problems and to improve risk assessment of complex extreme events in a vast array of applications—of which geo-environmental and climate applications are a major focus.
  • 2019: Laureate of the ENVR Early Investigator Award, from the Section on Statistics and the Environment (ENVR) of the American Statistical Association (ASA); click here for more details. The award citation reads:
    For the development of highly flexible models for extreme events observed in space and time and the detailed study of their joint tail behavior; for the development of novel computationally efficient methods for multivariate extreme value distributions; and for an exceptionally wide range of environmental and risk assessment applications.
  • 2019: Publication Lombardo et al. (2018), which appeared in the Stochastic Environmental Research and Risk Assessment (SERRA) journal, was highlighted among the top 10 most downloaded 2018 papers in Springer's Environmental Sciences Journals.
  • 2018: Award for Best 2016 Paper (Huser and Genton, 2016) published in the Journal of Agricultural, Biological and Environmental Statistics (JABES). The paper was presented in a special invited session at the International Biometric Conference (IBC) in Barcelona, Spain, 2018.
  • 2016: Elected Member of the International Statistical Institute (ISI).
  • 2015: Laureate of the Lambert Award for young statisticians, from the Swiss Statistical Society (SSS) to recognize the work of young statisticians up to age 35. The awarded work was presented in a plenary talk at the Swiss Statistics Meeting in Berne, Switzerland, 2015.
  • 2014: Laureate of the EPFL Doctorate Award 2014 (only two laureates amongst 403 Ph.D. theses defended university-wide), from the Swiss Federal Institute of Technology (EPFL), Lausanne, Switzerland. The award citation reads:
    For his contributions to the statistical modeling of extreme values, and in particular for his path-breaking study of space-time extremal rainfall, encompassing statistical theory, methods, and computations.
  • 2010: 1st prize: Ph.D. poster competition at the Workshop on Environmetrics, NCAR, Boulder CO, US.
  • 2009: 2nd prize: M.S. project poster competition, Institute of Mathematics, Swiss Federal Institute of Technology (EPFL), Lausanne, Switzerland.

Editorial activities



More details about editorial and reviewing activities on publons.

Why statistics of extremes?

I first got exposed to statistics of extremes when I was an undergraduate student in mathematics at the Swiss Federal Institute of Technology (EPFL), Lausanne, Switzerland. Since then, I have always been fascinated by the fact that there is a mathematically rigorous theory that precisely describes the probabilistic behavior of rare (i.e., low-probability) events, and that can be exploited in statistics to assess the risk of future, unprecedented extreme events, whose level may exceed our past experience. What I like about statistics of extremes is that it has both solid mathematical foundations, as well as a wide range of interesting applications in various fields, including Climate Science and Finance among others.


In 2014, I joined KAUST as a Postdoctoral Research Fellow. In 2015, I accepted a faculty position at KAUST (CEMSE Division), where I have been since that time (first, as an Assistant Professor, now as an Associate Professor) working on different aspects of statistics of extremes and spatio-temporal statistics. Beyond the extraordinary research environment at KAUST, I enjoy the diverse multicultural community, which is part of the DNA of KAUST and makes it so unique.


Raphaël Huser is an Associate Professor of Statistics in the Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division at the King Abdullah University of Science and Technology (KAUST), Saudi Arabia, where he leads the Extreme Statistics (extSTAT) research group. He started his career at KAUST initially as a Postdoctoral Research Fellow in 2014, and was later appointed Assistant Professor of Statistics, before transitioning to his current role as an Associate Professor in 2022. Before joining KAUST, Huser received his Ph.D. degree in statistics from the Swiss Federal University of Lausanne (EPFL) in 2013. He also holds a B.Sc. in mathematics and an M.Sc. in applied mathematics from EPFL. Huser has received several awards for his research work, including the 2014 EPFL Doctorate Award; the 2015 Lambert Award from the Swiss Statistical Association; the 2019 ENVR Early Investigator Award from the Section on Statistics and the Environment (ENVR) of the American Statistical Association (ASA); and more recently the 2022 Abdel El-Shaarawi Early Investigator Award from The International Environmetrics Society (TIES). He is also an Elected Member of the International Statistical Institute (ISI), and is currently serving as an Associate Editor for five top-tier statistics journals. Huser's research focuses on the development of new flexible and theoretically-motivated statistical models, as well as computationally efficient statistical machine learning inference methods, for extreme events in complex systems arising in various applications from climate and earth sciences, (crypto-)finance, and neuroscience. His work aims at making an impact in statistics of extremes and beyond, by improving models, prediction, and quantification of risk based on high-dimensional, multivariate and/or spatio-temporal, non-stationary datasets.