Extreme events (such as heavy rainfall, heat waves, or stock market crashes) behave fundamentally differently from averages. In the Extreme Statistics Research Group, we develop novel statistical models to better understand the stochastic behavior of such rare events, in order to assess and better predict their potential impact.
Raphaël Huser

Location

  • Building 1 (Al-Khawarizmi), Level 4, office 4125

Raphaël Huser is an Assistant Professor of Statistics and the Principal Investigator of the Extreme Statistics (extSTAT) 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. He then worked as a Postdoctoral Research Fellow at KAUST from January, 2014, to March, 2015.

Areas of expertise and current scientific interests

Raphaël Huser’s research focuses on statistics of extreme events and risk assessment, which includes the development of specialized models and methods with desirable statistical properties for complex spatio-temporal processes. Domains of applications include the modeling of natural hazards (e.g., heavy rainfall, heat or cold waves, strong wind gusts, devastating landslides), as well as financial risk (e.g., turbulence in stock markets).

Honors and Awards

  • 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.

Why KAUST?

In 2014, I joined KAUST as a Postdoctoral Research Fellow. In 2015, I accepted an Assistant Professor position at KAUST (CEMSE Division), where I have been since that time 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.