I am a research scientist at IBM working on stochastic modeling of natural resources. My career goals include:
- Advancing the field of machine learning and artificial intelligence to account for challenges unique to the Earth sciences
- Establishing new methodologies for uncertainty quantification and decision making involving the use of natural resources in the planet
In the past, I had the chance to work on very different mathematical problems, and that has certainly strenghtened my modeling skills:
B.Eng. in Mechanical Engineering @ UFPE (2007-2011) with emphasis on Computational Mechanics where I developed methods for propagating uncertainty from input to output parameters of physical simulation models. I had the chance to develop 3D finite element solvers for heat transfer and fluid flow, and the chance to develop uncertainty propagation routines based on polynomial chaos expansions and stochastic collocation.
M.Eng. in Civil Engineering @ UFPE (2011-2014) where I approached the history matching problem from the petroleum industry with inverse problem theory. I had the chance to develop Markov chain Monte Carlo routines for estimating input parameters of physical simulation models based on measurements of output parameters, and the chance to modify and optimize these routines for execution in distributed-memory computer clusters.
Ph.D. in Geostatistics @ Stanford University (2014-2018) where I developed new methods to simulate Earth-surface morphodynamics from experimental times series of image data. I had the chance to combine geostatistics and mathematical morphology in a stochastic model of surface dynamics, and the chance to calibrate this model with real laboratory data.
In this website, I plan to highlight some of the projects that I am working on, and share what I am learning as blog posts. Feel free to follow my post updates in your RSS feed reader using the link at the bottom of this page.