Alexander Engelhardt is a data scientist with 11 years of experience who combines a PhD in computational statistics from LMU München with practical freelance consulting work through Alpha Epsilon. He specializes in machine learning and algorithmic rigor, having implemented efficient, parallelized methods for large-scale analyses and contributed to the well-known mlr machine learning library in R by fixing bugs, adding tests, and refactoring key functions. Comfortable translating complex math into actionable insights, he has a track record across academic, consulting, and applied projects—from predictive models for biology to financial and clinical analyses. Based in Shinjuku, Japan, he pairs deep technical fluency with clear communication for clients and collaborators, and his GitHub persona hints at a dry, developer-minded sense of humor.
Contributions:8 commits, 10 PRs, 31 comments in 4 months
Contributions summary:Alexander contributed significantly to the `mlr-org/mlr` repository, a machine learning library in R. Their contributions focused on improving the functionality and reliability of the library. Specifically, they addressed bugs in the `smote` function, added tests for the `calculateConfusionMatrix` function, and refactored the `measureMultilabelF1` function. They also implemented a new parameter for the `calculateConfusionMatrix` function.
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