If I had to summarize the object of my research in one question, that would be: how can we quantify the state of knowledge we have about the Earth, given the different types of measurements that we make at the surface? This includes solving an inverse problem and finding a model of the Earth that explains our observations.
But most importantly, I am interested in quantifying uncertainties and trade-offs, and exploring the level of resolution associated with different data types and inverse schemes. I have been mainly working on Bayesian (i.e. probabilistic) inverse methods where the solution is a probability density function describing the information we have about the Earth. The goal is to fully take into account observational and theoretical errors, and to propagate them towards model uncertainties.
Although I have been mainly interested in inverse theory, I am also interested in the structure and evolution of the Earth. For example, by combining information from two global mantle models of shear wave velocity and attenuation, together with constraints from high-pressure experiments, we recently discovered the pervasive presence of partial melt below oceanic tectonic plates. Our observations suggest that by reducing viscosity, melt facilitates plate motion, and this has important implications for our understanding of plate tectonics and Earth dynamics.