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Raul Tempone


Raul Tempone was born in Montevideo, Uruguay, in 1969. He received the B.E. degree in Mechanical and Industrial Engineering at the University of the Republic, Montevideo, Uruguay in 1995. After his graduation he worked on the optimal dispatch of electricity for the Uruguayan system using techniques from nonlinear stochastic programming and visited the Royal Institute of Technology (KTH) in Stockholm, Sweden, to study further numerical analysis funded by the Swedish Institute. He obtained a MSc in Engineering Mathematics in 1999 (inverse problems for incompressible flows, supervised by Jesper Oppelstrup, KTH) and a PhD in Numerical Analysis in 2002 (a posteriori error estimation and control for stochastic differential equations, supervised by Anders Szepessy, KTH). He later moved to ICES, UT Austin, to work as a postdoctoral fellow from 2003 until 2005 in the area of numerical methods for PDEs with random coefficients (supervised by Ivo Babuska). In 2005 he became an assistant professor with the School of Computational Sciences and the Department of Mathematics at Florida State University, Tallahassee. In 2007 he was awarded the first Dahlquist fellowship by KTH and COMSOL for his contributions to the field of numerical approximation of deterministic and stochastic differential equations. In 2009 he joined KAUST as an Associate Professor in Applied Mathematics (founding faculty). He later became there the Director of the KAUST Center for Uncertainty Quantification and was promoted to the rank of Full Professor in 2015.


Dr. Tempone's research interests are in the mathematical foundation of computational science and engineering. More specifically, he has focused on a posteriori error approximation and related adaptive algorithms for numerical solutions of various differential equations, including ordinary differential equations, partial differential equations, and stochastic differential equations. He is also interested in the development and analysis of efficient numerical methods for uncertainty quantification and Bayesian based inverse problems and model validation. The areas of application he considers include, among others, engineering, chemistry, biology, physics as well as social science and computational finance.