Felipe Hofmann
Software Development Engineer at DataCebo
Los Angeles, California, United States
Summary
🤩
Rockstar🎓
Top SchoolFelipe Hofmann is a Software Development Engineer with nine years of experience building robust back-end systems and production-ready ML components, currently based in Los Angeles and working at DataCebo. He has a strong research foundation from MIT and a long tenure as a research assistant, translating academic rigor into practical engineering. Felipe is an active open-source contributor to synthetic data projects (SDV, CTGAN, Copulas), where he improved build infrastructure, stabilized models like CTGAN/TVAE, and added conditional sampling and packaging refinements that eased installation and reproducible releases. He combines DevOps-minded automation with hands-on ML engineering to push research-grade algorithms into usable libraries. An underappreciated strength is his focus on cross-platform build reliability and dependency management, which consistently reduces friction for downstream users and maintainers.
9 years of coding experience
6 years of employment as a software developer
URIITAPoliedroElectrical Engineering and Computer Science, Electrical Engineering and Computer Science at MITElite
Github Skills (25)
data-generation10
pytorch10
github-ci10
pytest10
python10
pandas10
machine-learning10
conda10
datatable10
generative-adversarial-network10
cicd10
generative-ai10
tabular10
version-control10
githubaction-workflow10
Programming languages (2)
Github contributions (5)
sdv-dev/CTGAN
Oct 2020 - Jan 2022
Conditional GAN for generating synthetic tabular data.
Role in this project:
Data Scientist
Data ScientistContributions:1 release, 48 reviews, 44 commits in 1 year 2 months
Contributions summary:Felipe primarily contributed to the improvement and stabilization of the CTGAN model. They addressed the instability of the gumbel_softmax function, creating workarounds and eventually a dedicated function to handle the issue. Furthermore, they made changes related to handling NaN values in categorical columns and updated documentation, showcasing their involvement in improving the model's robustness and usability. They also added unit tests to increase code coverage.
pytorchdeep-learningconditionaltabular-datagenerative-adversarial-network
sdv-dev/Copulas
Dec 2020 - May 2022
A library to model multivariate data using copulas.
Role in this project:Back-end & DevOps Engineer
Back-end & DevOps EngineerContributions:2 releases, 42 reviews, 21 commits in 1 year 5 months
Contributions summary:Felipe primarily focused on improving the project's infrastructure and build processes, as shown in updates to GitHub Actions and conda instructions, which included version constraints. They contributed to the project's development workflow by updating test configurations, fixing platform-specific issues, and managing the project's release and versioning. The user made changes to setup files, ensuring library compatibility, and addressing build processes.
pythontabular-datagenerative-modelmachine-learningdataset-generation
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