English:
Proficient
Marcelo V.
Vetted by YouTeam
Brazil
UTC -03:00
America/Recife
English:
Proficient
Versatile and proactive, with a background in developing, validating and deploying numerical, statistical, and data-driven solutions for data problems
- I developed a Bayesian inference for cosmological model fitting. - I developed a machine learning Type Ia Supernovae classification engine. - I advised a project on data-driven Type Ia Supernovae redshift estimation. - I advised a project on cosmological model fitting with a Bayesian approach. - I taught Python classes to graduate students in physics. - I taught Bayesian statistics classes to physics graduate students. - I taught machine learning classes to physics graduate students. - I developed a Monte Carlo FRB simulation Python package for the BINGO telescope collaboration (bingotelescope.org).
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Check if Marcelo is availableExpertise
Years of commercial development experience
11 years of experience
Core technologies
Other technologies
Project Highlights
FRBlip, a new FRB Monte Carlo mock catalog generator code
A Monte Carlo FRB simulation Python package for the BINGO telescope collaboration (bingotelescope.org); 10+ peer-reviewed publications.
Responsibilities & achievements
High-level technical and computer skills with an ability to design, implement and validate complex technical and scientific software. Conducted in-depth scientific research as a group leader.
Machine Learning, Programming and Statistical Classes
Developed and taught lectures on Python, Bayesian Statistics, and Machine Learning for graduate students. Lectures are available at github.com/mvsantosdev.
Responsibilities & achievements
Member of examination boards judging graduate school students. Lectured classes and prepared educational material for several advanced courses. Mentoring students.
Supernovae Classification and inference with Machine Learning and AutoML
Developed machine learning Type Ia Supernovae classification and regression codes, 2 peer-reviewed publications.
Responsibilities & achievements
Feature extraction from time series coming from astronomical data, in which the relevant features were chosen by a combination of expert knowledge and data-driven methodologies, including modeling, regression, and wavelet decomposition.
Numerical Simulations for f(R) Theories
Modified the Isis FORTRAN code to perform cosmological struct formation for modified gravity; 1 peer-reviewed publication.
Responsibilities & achievements
Construct intelligible reports synthesizing useful information extracted from data and current literature.
Cosmological Bayesian inferecen with Metropolis-Hastings Monte Carlo
Developed a Bayesian model fitting code and applied it to cosmological models; 4 peer-reviewed publications.
Responsibilities & achievements
Construct intelligible reports synthesizing useful information extracted from data and current literature.
Education
Higher education in Computer Science
Agency
400+
GMT-11
Remote
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Check if Marcelo is available