Miriam Simone is an AI engineer with 11 years of industry experience who blends a PhD in algebraic geometry with hands-on ML engineering across search, NLP, OCR, and vision systems. She has driven production ML at companies from SEEK and PlanGrid to StyleSeat and Squelch, building custom vector search, search relevance models in PyTorch, and OCR/object-detection pipelines. Currently focused on AI-first product development at Innergy and exploring LLM-driven developer workflows as a Gauntlet AI fellow, she’s particularly interested in making advanced math more accessible through educational applications. An active contributor to scikit-learn (notably improving export_graphviz behavior and Python 3 compatibility), she pairs deep theoretical pattern-recognition with pragmatic engineering in Python, Swift, and modern AI APIs. Off the clock she teaches kids Brazilian Jiu-Jitsu, plays jazz, and ferments things—interests that echo her fascination with recurring structures across math, music, and microbiology.
Contributions:15 commits, 9 PRs, 18 comments in 16 days
Contributions summary:Miriam contributed to the decision tree module, focusing on enhancements to the `export_graphviz` function, particularly concerning its behavior when `out_file` is set to `None` or the default value. Their changes included updating example code to use `pydotplus` for compatibility with Python 3 and resolving linting issues. The user also addressed the return type of `export_graphviz` and fixed indentation issues, along with making sure that the depreciation warning for the `out_file` parameter is appropriately shown.
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