I am a research scientist with more than 10 years of experience in advanced statistical theories for geosciences. I am the author and lead developer of the GeoStats.jl project, as well as various other open source projects that are widely used by geoscientists around the world.

My career goals include:

  1. Advancing the field of machine learning and artificial intelligence to account for challenges that are unique to the geosciences
  2. Establishing new methodologies for uncertainty quantification and decision making involving the use of natural resources in the planet

To achieve these goals, I’ve founded Arpeggeo®.

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.

Work experience

  • [2022–present] CEO, Arpeggeo®, Rio de Janeiro, BRA.
  • [2018–2020] Research Scientist, IBM Research, Rio de Janeiro, BRA.
  • [2017] Teaching Assistant, Stanford University, California, USA.
  • [2016] Summer Intern, Landmark Solutions, Texas, USA.


  • [2021–2022] IMPA Postdoc, Industrial Mathematics
    • Topic: Geostatistical Learning
  • [2014–2018] Stanford University Ph.D., Geostatistics
    • Thesis: Morphodynamic Analysis and Statistical Synthesis of Geomorphic Data: Application to a Flume Experiment
    • Adviser: Jef Caers
  • [2011–2014] UFPE M.Eng., Civil Engineering
    • Dissertation: The Inverse Problem of History Matching
    • Adviser: Ramiro Willmersdorf
  • [2007–2011] UFPE B.Eng., Mechanical Engineering
    • Course project: A 3D Finite Element Analysis Solver
    • Adviser: Ramiro Willmersdorf


  • [2022] Frontiers in Applied Mathematics and Statistics Award
  • [2021] IMPA Fellowship
  • [2018] Syvitski Modeler Award
  • [2013] SwB Scholarship
  • [2012] PRH-26/Petrobras Scholarship
  • [2007] PRH-26/ANP Scholarship