Experienced in applying Machine Learning and Bayesian inference techniques to studies of big datasets from astrophysical systems, such as gravitational lenses. Skilled at interpreting and explaining complex findings. Team lead in the international scientific collaboration Dark Energy Survey, with proven ability to execute projects from end to end. Published papers in major journals.
Leading a team of 5 scientists analyzing data of about 300 million galaxies.
Designed and implemented a pipeline using unsupervised learning and bayesian inference to estimate the redshift of galaxies.
Rewarded with exclusive data access rights and co-authored over 30 publications.
Samplers and Tensions | 10/2019 - 09/2021
Co-led a team of 8 scientists assessing the consistency between data collected by independent experiments.
Generated simulated data to evaluate the efficiency of statistical metrics in identifying inconsistencies amongst measurements of cosmological parameters in high dimensional parameter spac
Recommended the Dark Energy Survey Year 3 analysis strategy and published results in renowned journal.
Developed and tested method to use empirical blinded data to perform model selection.
Applied method to the problem of intrinsic alignments – one of the most significant weak lensing systematics, and a major contributor to the error budget in modern lensing surveys.
Found a 30% chance of biased posterior by more than 0.3when assuming the current prevalent intrinsic alignment model.
Universidade Federal de São João del-Rei, Brazil | Lecturer | 09/2015 - 12/2015
Lectured the course Fundamentals of Physics I and Introductory Experimental Physics to ~ 70 engineering undergraduates.
Designed lectures, homework assignments and exams.
Skills
Programming & Scripting Languages: Python (5+ years of experience) | Fortran | Bash | SQL