Open source · Mar 2026 — present
Contributing to a probabilistic ML library — 1.2k+ stars, 134k+ monthly PyPI downloads — used in production forecasting pipelines. Four merged pull requests, including a fix to a mathematical bug in the loss computation itself.
Found and fixed a critical mathematical bug in SquaredDistrLoss: the log-pdf was being used in place of the pdf inside the loss computation, silently producing incorrect loss values for every downstream probabilistic model built on it. In a library this widely deployed, that's the kind of bug that quietly skews results for everyone using it until someone traces it back to the source.
Refactored parameter validation in CyclicBoosting, eliminating over 60 lines of duplicated conditional logic by centralizing it into a single helper function — while maintaining 100% test coverage across the change.
Across PR #813, #833, #917, and #934, the work spans correctness fixes, validation refactoring, and CI improvements — the unglamorous maintenance that keeps a production ML library trustworthy.