Working in mathematics is an adventure. I seek the thrill of discovery—always visiting new places with the mind, searching for simplicity, beauty and truth. Developing theory and problem-solving are essential; keeping applications present gives meaning to our excursions.
- Building 4, Office 4109
- Raul Tempone's courses
- Ph.D. Numerical Analysis, Royal Institute of Technology, 2002
- M.S. Engineering Mathematics, Universidad de la Republica, Montevideo, Uruguay, 1999
- B.S. Industrial and Mechanical Engineering, Universidad de la Republica, Montevideo, Uruguay, 1995
Professor Raul Fidel Tempone is an internationally known expert in Statistics and Uncertainty Quantification (UQ). In KAUST, Professor Tempone has established the Stochastics Numerics Research Group that focuses on developing efficient and robust numerical methods for solving stochastic differential equations in engineering and sciences. The Group's work includes numerical analysis, computational mechanics, mathematical finance, biological modeling and network theory which are involved with stochastics.
Education and early career
Raul Tempone received his Ph.D. from the Royal Institute of Technology in Stockholm in 2002. He then worked as Postdoctoral Fellow at the Institute for Computational and Engineering Sciences (ICES) at the University of Texas, Austin, and as an Assistant Professor at Florida State University, Tallahassee. From 2009, Tempone is a Full Professor at King Abdullah University of Science and Technology in Thuwal, Saudi-Arabia.
Areas of expertise and current scientific interests
Raul 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 optimal control, uncertainty quantification and Bayesian model calibration, validation, and optimal experimental design. The areas of application he considers include, among others, engineering, chemistry, biology, physics as well as social science and computational finance.
Why Uncertainty Quantification?
The aim of uncertainty quantification is to assess the sources of error and uncertainties in complex situations where not all the individual parameters are known. Events may be influenced by small differences that can produce extreme differences of scenarios. In my work, I am pioneering quantitative models for complex situations using stochastic analysis and modeling.
KAUST has understood the importance of Uncertainty Quantification, control theory, and numerical analysis as fundamental disciplines for optimizing industrial processes and controls, offering me the opportunity to work and breaking new ground.