High Performance Computing (HPC) allows scientists and engineers to solve complex problems using software that requires high bandwidth, low latency networking, high memory and high computing capabilities. This kind of software is often dedicated to numerical simulations in many fields such as meteorology, oceanography, computational finance, etc.. Over the last few decades, HPC has extended the range of phenomena that can be investigated within these research fields.

Variational data assimilation consists in estimating control parameters of a numerical model in order to minimize the misfit between the forecast values and the actual observations. This gradient based technique is used in geophysics inversion problems. To be applied, it first needs the computation of the adjoint model (transposed Jacobian matrix of the numerical model) a step usually expensive from an implementation point of view.

My research spans the area of HPC and variational data assimilation. The common thread throughout my work is the design and implementation of original software to solve complex problems. I have been working in research teams with a focus on applied mathematics, one of my goals being to work closely to the real world applications.