Guilherme Bodin
Head Of R&D at PSR - Energy Consulting and Analytics
Rio de Janeiro, Brazil
Summary
👤
Senior🎓
Top SchoolGuilherme Bodin is Head of R&D at PSR in Rio de Janeiro, bringing eight years of experience at the intersection of energy systems, optimization, and applied statistics. With an M.Sc. in Electrical Engineering (Operations Research) from PUC-Rio and a background in academic R&D at LAMPS, he specializes in time series modeling, optimization under uncertainty, and analytics for hydrothermal power markets. He progressed from software engineer to team leader and now leads research strategy, shaping software development practices and mentoring teams. An active open-source contributor, he maintains tools in the JuMP ecosystem (including a Google Summer of Code project for automatic dualization) and has improved core JuMP functionality around expression sums and solver ergonomics. Colleagues rely on him for translating complex mathematical models into robust production code that informs real-world power contracting and pricing decisions.
8 years of coding experience
4 years of employment as a software developer
Master of Science - MS, Master of Science - MS at Ecole Centrale de MarseillePontifical Catholic University of Rio de Janeiro
Portuguese, English, French, Spanish
Github Skills (9)
mathematical10
modeling10
julia10
optimization10
nonlinear-programming9
mathematical-programming9
mixed-integer-programming9
linear-programming9
testing7
Programming languages (8)
Github contributions (5)
jump-dev/JuMP.jl
Mar 2019 - Sep 2021
Modeling language for Mathematical Optimization (linear, mixed-integer, conic, semidefinite, nonlinear)
Role in this project:
Back-end Developer
Back-end DeveloperContributions:7 commits, 7 PRs, 20 comments in 2 years 6 months
Contributions summary:Guilherme's contributions primarily revolve around enhancing the core functionality of the modeling language. They focused on implementing and generalizing sum operations for mathematical expressions, improving the handling of silent optimization, and adding a function to measure solve time. The user also addressed printing errors in IJulia mode and generalized the model type for constraint references. These changes suggest a focus on improving the library's numerical capabilities and user experience.
conic-programsmathematicallinear-programmingnonlinear-programminglinear
LAMPSPUC/ForecastAccuracy.jl
Dec 2019 - Jul 2020
Implementation of forecast accuracy measures
Contributions:12 commits, 17 PRs, 21 pushes in 7 months
forecastmeasuresaccuracy
Find and Hire Top DevelopersWe’ve analyzed the programming source code of over 60 million software developers on GitHub and scored them by 50,000 skills. Sign-up on Prog,AI to search for software developers.